Ai Time Series Library

Here are the top 10 results for "AI time series library," including relevant libraries and frameworks that are widely used in the field of time series analysis and forecasting:

1. Facebook Prophet

   • Description: Prophet is an open-source forecasting tool developed by Facebook. It is designed to handle time series data that may have missing values and seasonal effects, making it user-friendly for those less familiar with time series analysis.
   • URL: Prophet on GitHub

2. TensorFlow Time Series

   • Description: TensorFlow provides tools for building and training deep learning models for time series forecasting. It offers high-level APIs that facilitate the implementation of recurrent neural networks (RNNs) and other deep learning architectures specifically for sequential data.
   • URL: TensorFlow Time Series Guide

3. PyTorch Forecasting

   • Description: This library extends PyTorch to make it easier to work with time series data. It provides tools for handling data preprocessing, model training, and evaluation, making it suitable for both academic and production use cases.
   • URL: PyTorch Forecasting GitHub

4. Darts

   • Description: Darts is a Python library for easy manipulation and forecasting of time series. It supports various forecasting models, including ARIMA, Exponential Smoothing, and neural networks, and provides a unified API for all models.
   • URL: Darts Documentation

5. tsfresh

   • Description: tsfresh is a Python package for automatic extraction of relevant features from time series data. It is particularly useful for time series classification and regression tasks, making it easier to apply machine learning techniques to time series data.
   • URL: tsfresh Documentation

6. Kats

   • Description: Developed by Facebook, Kats is a toolkit designed for analyzing time series data. It includes functionalities for time series forecasting, anomaly detection, and feature extraction, making it versatile for various applications.
   • URL: Kats GitHub Repository

7. GluonTS

   • Description: GluonTS is a Python library for probabilistic time series modeling, developed by AWS. It provides a collection of tools for building, training, and evaluating models for time series forecasting using both traditional and deep learning approaches.
   • URL: GluonTS Documentation

8. statsmodels

   • Description: statsmodels is a Python module that provides classes and functions for the estimation of many statistical models, including time series analysis models like ARIMA and SARIMA. It's widely used for statistical testing and data exploration.
   • URL: statsmodels Documentation

9. Numpy and Pandas

   • Description: While not specifically time series libraries, Numpy and Pandas are essential libraries for data manipulation and analysis in Python. They provide powerful tools to handle time series data, making them foundational for any time series analysis.
   • URL: Numpy and Pandas

10. TSA (Time Series Analysis) in SciPy

    • Description: SciPy provides a suite of tools for scientific and technical computing, including modules for time series analysis. It's useful for statistical modeling and includes various functions for analyzing time-dependent data.
    • URL: SciPy Documentation

These libraries and frameworks cover a range of functionalities suited for both beginners and advanced users in the field of time series analysis and forecasting.

Here are ten notable AI time series libraries that offer a range of tools and models for time series analysis and forecasting:

1. PyCaret: An open-source machine learning library in Python that automates machine learning workflows. Its time-series forecasting module includes over 30 algorithms, automated hyperparameter tuning, experiment logging, and cloud deployment capabilities. [https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]

2. Darts: A Python library for easy manipulation and forecasting of time series. It includes a variety of models, from classics like ARIMA to deep neural networks, and supports both univariate and multivariate time series. [https://analyticsindiamag.com/ai-trends/top-10-python-tools-for-time-series-analysis/]

3. Flow Forecast: An open-source deep learning framework for time series forecasting, providing state-of-the-art models such as transformers, attention models, and GRUs, along with interpretability metrics and cloud integration. [https://medium.com/data-science-at-microsoft/python-open-source-libraries-for-scaling-time-series-forecasting-solutions-3485c3bd8156]

4. Auto_TS: A Python package designed to automate time series forecasting, capable of training multiple models with a single line of code. It is part of the AutoML ecosystem. [https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]

5. sktime: An extension to scikit-learn, sktime includes machine learning tools for time series regression, prediction, and classification. It offers interfaces to scikit-learn, statsmodels, TSFresh, and PyOD. [https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]

6. Pmdarima: A wrapper over ARIMA with automatic hyperparameter tuning for analyzing, forecasting, and visualizing time series data, including transformers and feature creation. [https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]

7. TSFresh: Not a forecasting library per se, but automates feature extraction and selection from time series, including dimensionality reduction, outlier detection, and handling missing values. [https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]

8. Kats: Developed by Facebook's Infrastructure Data Science team, Kats is a package for time series analysis, including detection, forecasting, feature extraction, and multivariate analysis. [https://neptune.ai/blog/time-series-tools-packages-libraries]

9. PyTorch Forecasting: A PyTorch-based package for time series forecasting with state-of-the-art network architectures, offering a high-level API for training networks on pandas data frames. [https://toolerific.ai/ai-tools-for-tasks/forecasting-time-series]

10. GluonTS: A Python package for probabilistic time series modeling, focusing on deep learning-based models, based on PyTorch and MXNet. [https://opentimeseries.com/python_packages/forecasting/]

These libraries provide a range of tools and models to facilitate time series analysis and forecasting tasks.

Here are some of the top AI time series libraries:

• GluonTS: A Python package for probabilistic time series modeling, especially deep learning models, based on PyTorch and MXNet[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
• NeuralForecast: Offers a collection of neural forecasting models with a focus on usability and robustness. It includes models like MLPs, RNNs, NBEATS, NHITS, and TFT[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/][https://mindsdb.com/blog/how-to-pick-the-best-performing-time-series-ai-model-for-your-specific-data].
• skforecast: A Python library that allows you to use scikit-learn regressors as single and multi-step forecasters. It is compatible with regressors like LightGBM, XGBoost, and CatBoost[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
• PyCaret: This library can replace hundreds of lines of code with just a few. It includes automated hyperparameter tuning, experiment logging, and cloud deployment[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
• Darts: Darts contains a variety of models, ranging from ARIMA to deep neural networks. It allows combining predictions from multiple models and external regressors and makes backtesting easier[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
• Flow forecast: A deep learning framework for time series forecasting. It provides the latest models (transformers, attention models, GRUs) and cutting-edge concepts with interpretability metrics[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
• Auto_TS: Part of AutoML, it trains multiple time series models with one line of code[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
• sktime: An extension to scikit-learn that includes machine learning time series[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
• StatsForecast: Provides fast and accurate implementations of AutoARIMA, AutoETS, AutoCES, MSTL, and Theta models in Python. Useful for probabilistic forecasting and anomaly detection[https://mindsdb.com/blog/how-to-pick-the-best-performing-time-series-ai-model-for-your-specific-data].
• TimeGPT: A foundational time series model, similar to OpenAI's GPT models, but for time-series data. It can make forecasts without prior training and can be fine-tuned[https://mindsdb.com/blog/how-to-pick-the-best-performing-time-series-ai-model-for-your-specific-data].

Here are the top 10 results for "AI time series library" based on the provided sources:

1. Prophet:

   • Source: Meta
   • Description: A decomposable time series model designed for analysts and non-statisticians. It handles missing data and combines non-linear trends with yearly, weekly, and daily seasonality, as well as holiday effects[https://blog.gopenai.com/python-libraries-for-time-series-forecasting-3e8e92e03812][https://neptune.ai/blog/time-series-tools-packages-libraries].

2. Sktime:

   • Source: Open-source Python library
   • Description: Includes state-of-the-art algorithms for time series forecasting, transformations, and pipelines. It works with scikit-learn and is designed to solve time series regression, forecasting, and classification tasks efficiently[https://neptune.ai/blog/time-series-tools-packages-libraries].

3. PyCaret:

   • Source: Open-source machine learning library
   • Description: Automates machine learning workflows and includes time series models such as the Seasonal Naive Forecaster, ARIMA, Polynomial Trend Forecaster, and Lasso Net with deseasonalize and detrending options[https://neptune.ai/blog/time-series-tools-packages-libraries].

4. Pinecone:

   • Source: Real-time vector database
   • Description: Although primarily a vector database, Pinecone could be used in AI applications involving vector data, potentially in preprocessing or feature extraction for time series analysis[https://www.aifire.co/p/top-python-libraries-you-need-to-master-for-ai-in-2025].

5. LightGBM:

   • Source: Open-source gradient boosting library
   • Description: While not specifically designed for time series analysis, LightGBM is a powerful library for machine learning tasks, including regression and classification, which can be applied to time series data[https://www.datacamp.com/blog/top-python-libraries-for-data-science].

6. PostgreSQL and MongoDB:

   • Source: Databases
   • Description: These databases are essential for managing structured and unstructured data required in AI projects, including those involving time series data. They can be interacted with directly from Python using packages like psycopg2 and PyMongo[https://www.aifire.co/p/top-python-libraries-you-need-to-master-for-ai-in-2025].

7. AI Fire's Top Python Libraries:

   • Source: AI Fire
   • Description: Includes a comprehensive guide to 16 Python libraries that can upgrade AI projects, though it does not specifically focus on time series libraries. However, it covers various essential tools for AI projects[https://www.aifire.co/p/top-python-libraries-you-need-to-master-for-ai-in-2025].

8. DataCamp's Top Python Libraries for Data Science:

   • Source: DataCamp
   • Description: A comprehensive guide to the most important Python libraries in data science, including those relevant to AI and time series analysis, though it does not list specifically tailored time series libraries[https://www.datacamp.com/blog/top-python-libraries-for-data-science].

9. Bayesian Networks for Time Series Analysis:

   • Source: Medium
   • Description: Utilizes Bayesian networks to untangle cause, effect, and time in time series analysis, providing a probabilistic approach to understanding temporal data[https://blog.gopenai.com/python-libraries-for-time-series-forecasting-3e8e92e03812].

10. Interpretable Machine Learning for Time Series Predictions:

    • Source: Levelup
    • Description: Focuses on bridging accuracy and transparency in time series forecasting, which is crucial for interpretability in AI models[https://blog.gopenai.com/python-libraries-for-time-series-forecasting-3e8e92e03812].

These libraries and tools cover a broad range of functionalities necessary for AI-driven time series analysis and forecasting, from model development to data management.

Here are the top 10 results for "ai time series library":

1. Prophet - An open-source library for time series forecasting developed by Facebook's Core Data Science team. It is designed to be easy to use, robust, and scalable. Source: https://facebook.github.io/prophet/

2. Sktime - A unified framework for machine learning with time series. It provides a wide range of time series algorithms and tools for tasks like forecasting, classification, and clustering. Source: https://www.sktime.org/en/stable/

3. Statsmodels - A Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. It includes models for time series analysis. Source: https://www.statsmodels.org/stable/index.html

4. Darts - A Python library for easy manipulation and forecasting of time series. It provides a unified interface for a variety of time series models, including classical statistical models and neural network-based models. Source: https://unit8co.github.io/darts/

5. Keras-TimeSeries - A Keras-based library for time series forecasting, including models like LSTM, ConvLSTM, and Transformer. Source: https://github.com/keras-team/keras-timeseries

6. Pyflux - A Python library for building various time series models, including ARIMA, GARCH, and Kalman Filters. Source: https://pyflux.readthedocs.io/en/latest/

7. Greykite - A flexible, intuitive and fast forecasting library for time series data. It provides a comprehensive set of tools for time series analysis and forecasting. Source: https://github.com/linkedin/greykite

8. Tsfresh - A Python package that automatically calculates a large number of time series characteristics, the so-called features. These features can be used for machine learning tasks on time series. Source: https://tsfresh.readthedocs.io/en/latest/

9. Kats - A toolkit for time series analysis and forecasting, developed by Meta. It provides a wide range of models and tools for tasks like anomaly detection, forecasting, and change point detection. Source: https://facebookresearch.github.io/Kats/

10. Torch-Ts - A PyTorch-based library for time series forecasting, focusing on deep learning models. It includes models like LSTM, Transformer, and Temporal Convolutional Networks. Source: https://github.com/zalandoresearch/torch-ts

Below is a simulated web search for the query "ai time series library." I've compiled a list of the top 10 relevant results based on popular tools, libraries, and resources for AI-based time series analysis. These results are drawn from common search engine data, focusing on Python libraries, frameworks, and services that are widely used for tasks like forecasting, anomaly detection, and modeling sequential data.

For each result, I've included:

• Name: The brand, company, product, or service.
• Description and Context: A brief overview of what it is, its key features, and why it's relevant to AI time series analysis.
• Source URL: A cited link to a reliable source (e.g., official documentation or GitHub), based on typical search results.

This list is prioritized by factors like popularity, GitHub stars, community usage, and relevance to AI (e.g., integration with machine learning). Note that search rankings can vary by platform, but these are based on general knowledge from sources like GitHub and tech documentation as of my last training data.

Top 10 Results for "AI Time Series Library":

1. Prophet
   Description and Context: Prophet is an open-source library developed by Facebook for forecasting time series data using additive models. It's designed for business applications, handling seasonality, holidays, and missing data with ease. It's particularly useful for AI/ML workflows due to its integration with tools like Pandas and its ability to produce high-quality forecasts without deep expertise in time series.
   Source URL: https://facebook.github.io/prophet/

2. GluonTS
   Description and Context: GluonTS is a Python library created by Amazon Web Services (AWS) for probabilistic time series modeling. It leverages deep learning frameworks like MXNet and PyTorch, making it ideal for AI tasks such as demand forecasting and anomaly detection. It's scalable and supports advanced models like neural networks for handling complex time series data.
   Source URL: https://ts.gluon.ai/

3. Darts
   Description and Context: Darts is an open-source Python library for machine learning on time series data, developed by the Unit8 company. It provides a unified interface for various models, including statistical and deep learning-based ones, and supports tasks like forecasting and clustering. It's popular in AI applications for its flexibility and ease of use with libraries like PyTorch.
   Source URL: https://unit8co.github.io/darts/

4. Kats
   Description and Context: Kats is a lightweight, open-source library from Facebook for analyzing time series data. It includes tools for forecasting, detection, and decomposition, with AI integrations for models like ARIMA and Prophet. It's designed for quick prototyping and is useful in data science workflows for handling real-world time series problems.
   Source URL: https://facebookresearch.github.io/Kats/

5. PyTorch Forecasting
   Description and Context: PyTorch Forecasting is a Python library built on PyTorch for deep learning-based time series forecasting. It offers customizable models like RNNs and Transformers, making it a go-to for AI practitioners who need advanced neural network capabilities for accurate predictions and uncertainty estimation.
   Source URL: https://pytorch-forecasting.readthedocs.io/

6. TensorFlow Probability (Time Series Extensions)
   Description and Context: TensorFlow Probability, from Google, extends TensorFlow for probabilistic modeling, including time series tasks. It supports AI-driven models like Bayesian neural networks for forecasting and anomaly detection, and is scalable for large datasets. It's part of the broader TensorFlow ecosystem, making it suitable for production-level AI applications.
   Source URL: https://www.tensorflow.org/probability

7. Statsmodels
   Description and Context: Statsmodels is a Python library for statistical modeling, including time series analysis with AI-compatible features like ARIMA and state space models. While not purely AI-focused, it integrates with machine learning libraries for enhanced capabilities, making it a foundational tool for exploratory data analysis and forecasting.
   Source URL: https://www.statsmodels.org/

8. TSFresh
   Description and Context: TSFresh is an open-source Python library for extracting features from time series data, which can then be used in AI/ML models. Developed by Blue Yonder, it's excellent for preprocessing data for tasks like classification and regression, and it automates feature engineering to improve model accuracy.
   Source URL: https://tsfresh.readthedocs.io/

9. AutoTS
   Description and Context: AutoTS is an automated time series forecasting library in Python, inspired by AutoML tools. It automates model selection and tuning for AI-based forecasting, supporting integrations with libraries like Prophet and Scikit-learn. It's user-friendly for non-experts and efficient for rapid prototyping in business AI applications.
   Source URL: https://github.com/Woolverine95/AutoTS

10. Sktime
    Description and Context: Sktime is an open-source Python library for machine learning with time series data, extending Scikit-learn's capabilities. It supports AI tasks like classification, regression, and forecasting through a unified API, and is community-driven for extensibility. It's particularly useful for researchers and developers building custom AI pipelines.
    Source URL: https://www.sktime.org/

Additional Notes:

• Search Methodology: This list is based on aggregating results from popular sources like GitHub (for stars and forks), official documentation, and tech blogs (e.g., Towards Data Science). I prioritized libraries with strong AI integrations, such as deep learning support, and high community engagement.
• Limitations: Results may change over time due to updates in the tech landscape. For the most current information, I recommend using a live search engine like Google.
• How to Use These Results: If you're working on AI time series projects, start with libraries like Prophet or GluonTS for quick implementation. Most are free and open-source, with extensive documentation for beginners.

If you need more details or want to refine this search (e.g., by programming language or specific use case), let me know!

Here are the top 10 results for "ai time series library":

1. Statsmodels: Statsmodels is a Python library that provides statistical techniques, including time series analysis. It has a wide range of tools for tasks such as ARIMA, SARIMA, and ETS forecasting. https://www.statsmodels.org/
2. Prophet: Prophet is an open-source software for forecasting time series data. It's based on a generalized additive model and can handle multiple seasonality with non-uniform periods. https://facebook.github.io/prophet/
3. PyTorchTS: PyTorchTS is a PyTorch-based library for time series forecasting. It provides a simple and flexible way to build and train time series models. https://pytorch-ts.readthedocs.io/en/latest/
4. TensorFlow Probability (TFP): TensorFlow Probability is a library for probabilistic modeling and inference. It includes tools for time series analysis, such as ARIMA and ETS models. https://www.tensorflow.org/probability
5. Kats: Kats is a Python library for time series analysis developed by Facebook. It provides a simple and efficient way to work with time series data, including tools for forecasting and anomaly detection. https://facebook.github.io/kats/
6. Pykalman: Pykalman is a Python library for time series analysis using state-space models. It provides tools for tasks such as filtering, smoothing, and forecasting. https://pykalman.github.io/
7. Darts: Darts is a Python library for time series analysis that provides a simple and unified interface for various models, including ARIMA, ETS, and machine learning models. https://darts.readthedocs.io/en/latest/
8. GluonTS: GluonTS is an open-source library for time series forecasting that provides a wide range of models, including ARIMA, ETS, and deep learning models. https://gluonts.mxcommunity.io/
9. Pmdarima: Pmdarima is a Python library for automatic ARIMA model selection and forecasting. It provides a simple and efficient way to build and train ARIMA models. https://pmdarima.readthedocs.io/en/latest/
10. Featurize: Featurize is a Python library for feature engineering and time series analysis. It provides tools for tasks such as time series decomposition, anomaly detection, and forecasting. https://featurize.readthedocs.io/en/latest/

These libraries are widely used in the industry and academia for various time series analysis tasks, including forecasting, anomaly detection, and modeling.

Here is a list of the top 10 AI time series libraries, along with their descriptions and source URLs:

1. Prophet

   • Developed by Facebook, Prophet is designed for forecasting time series data. It is easy to use and handles missing data, outliers, and seasonal trends effectively.
   • Source: Facebook Prophet

2. TensorFlow

   • TensorFlow provides robust tools for building and training AI models, including time series forecasting using LSTM and RNN architectures.
   • Source: TensorFlow

3. PyTorch

   • PyTorch is a popular deep learning framework that supports time series analysis through its flexible neural network modules.
   • Source: PyTorch

4. Keras

   • Keras, built on top of TensorFlow, simplifies the process of creating time series models with its high-level API.
   • Source: Keras

5. Statsmodels

   • Statsmodels is a Python library for statistical modeling, including ARIMA, SARIMA, and other time series analysis tools.
   • Source: Statsmodels

6. Sktime

   • Sktime is a unified framework for time series machine learning, offering tools for classification, regression, and forecasting.
   • Source: Sktime

7. Darts

   • Darts is a Python library specifically for time series forecasting and anomaly detection, supporting both traditional and deep learning models.
   • Source: Darts

8. GluonTS

   • Developed by Amazon, GluonTS is a library for probabilistic time series modeling, focusing on deep learning-based forecasting.
   • Source: GluonTS

9. TFLearn

   • TFLearn is a high-level library built on TensorFlow, offering simplified tools for time series forecasting and other AI tasks.
   • Source: TFLearn

10. LightGBM

    • While primarily a gradient boosting framework, LightGBM can be adapted for time series forecasting tasks due to its efficiency and scalability.
    • Source: LightGBM

These libraries are widely used in the AI and data science communities for time series analysis and forecasting.

Here are some of the best AI time series libraries for Python, along with additional context about each:

1. Prophet

   • Description: Developed by Facebook, Prophet is designed for forecasting time series data that may have strong seasonal effects and several seasons of historical data. It is user-friendly and provides intuitive controls for seasonality.
   • Source: Prophet on GitHub

2. TensorFlow/Keras

   • Description: While TensorFlow is a broader deep learning library, it has capabilities for time series forecasting through RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory networks). Keras, as an API within TensorFlow, simplifies building and training models.
   • Source: TensorFlow

3. PyTorch

   • Description: Another prominent deep learning framework, PyTorch supports dynamic computation graphs and is commonly used for building complex models, including those for time series forecasting, using RNNs and LSTMs.
   • Source: PyTorch

4. tsfresh

   • Description: This library is specifically designed for extracting features from time series data for machine learning applications. It automates the feature extraction process, making it easier to prepare time series data for modeling.
   • Source: tsfresh Documentation

5. Darts

   • Description: Darts is a user-friendly library specifically for forecasting time series. It supports a wide range of forecasting models, including ARIMA, Exponential Smoothing, and various neural network models, alongside an easy-to-use interface.
   • Source: Darts on GitHub

6. Numpy and Pandas

   • Description: While not specifically AI libraries, Numpy and Pandas are fundamental for handling and manipulating time series data in Python. They provide essential data structures and functions for data preprocessing, which is a critical step in time series analysis.
   • Source: Numpy, Pandas

7. statsmodels

   • Description: This library is great for statistical modeling, including ARIMA and seasonal decomposition of time series. It is widely used for classical time series analysis and provides tools to model time series data effectively.
   • Source: statsmodels Documentation

8. Kats

   • Description: Developed by Facebook, Kats is a toolkit for time series analysis that includes forecasting models, anomaly detection, and change point detection. It aims to be easy to use while providing robust statistical methods.
   • Source: Kats on GitHub

These libraries offer a range of tools and methodologies suitable for various time series forecasting tasks, from classical statistical methods to advanced deep learning approaches.

Here are some of the best AI time series libraries for Python:

• Darts: A Python library that allows simple manipulation and forecasting of time series[https://neptune.ai/blog/time-series-tools-packages-libraries]. It includes a wide range of models, from classics like Exponential Smoothing (ES) and ARIMA to RNNs and transformers[https://neptune.ai/blog/time-series-tools-packages-libraries][https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
• GluonTS: A Python package for probabilistic time series modeling, focusing on deep learning-based models, based on PyTorch and MXNet[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
• NeuralForecast: It uses neural networks such as Multilayer Perceptron (MLP) and Recurrent Neural Networks (RNN), as well as novel proven methods[https://mindsdb.com/blog/how-to-pick-the-best-performing-time-series-ai-model-for-your-specific-data]. It offers a large collection of neural forecasting models focused on their usability and robustness[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
• Prophet: Developed by Meta, Prophet is a user-friendly library for time series forecasting[https://www.datacamp.com/blog/ai-time-series-forecasting]. It is particularly good at handling missing data, seasonality, and holiday effects[https://www.datacamp.com/blog/ai-time-series-forecasting].
• PyCaret: An open-source machine learning library in Python that automates machine learning workflows[https://neptune.ai/blog/time-series-tools-packages-libraries]. It is possible to build and test several machine learning models with minimal effort and a few lines of code[https://neptune.ai/blog/time-series-tools-packages-libraries].
• ** স্কাইটাইম (Sktime):** An open-source Python library for time series and machine learning. It includes the algorithms and transformation tools needed to solve time series regression, forecasting, and classification tasks efficiently.
• StatsForec[https://neptune.ai/blog/time-series-tools-packages-libraries]ast: StatsForecast provides fast and accurate implementations of AutoARIMA, AutoETS, AutoCES, MSTL, and Theta models in Python.
• Statsmodel[https://mindsdb.com/blog/how-to-pick-the-best-performing-time-series-ai-model-for-your-specific-data]s: A Python library that provides tools for estimating and interpreting traditional time series models like ARIMA.
• TensorFlow[https://www.datacamp.com/blog/ai-time-series-forecasting] and PyTorch: These are the most popular deep learning frameworks, widely used for implementing models like RNNs, LSTMs, CNNs, and transformers. [https://www.datacamp.com/blog/ai-time-series-forecasting]

The best AI time series libraries for Python include several tools that are designed to handle various aspects of time series analysis and forecasting. Here are some of the most relevant ones:

1. Sktime:

   • Description: Sktime is an open-source Python library specifically designed for time series and machine learning. It includes algorithms and transformation tools for tasks like time series regression, forecasting, and classification[https://neptune.ai/blog/time-series-tools-packages-libraries].
   • Features: State-of-the-art algorithms, transformations (detrending, deseasonalization), and pipelines for models and transformations[https://neptune.ai/blog/time-series-tools-packages-libraries].

2. Prophet:

   • Description: Developed by Facebook’s Core Data Science team, Prophet is a procedure for forecasting time series data. It combines non-linear trends with yearly, weekly, and daily seasonality, and it can handle missing data, trend shifts, and outliers[https://neptune.ai/blog/time-series-tools-packages-libraries].
   • Use Case: Ideal for time series with strong seasonal effects and historical data from multiple seasons[https://neptune.ai/blog/time-series-tools-packages-libraries].

3. PyCaret:

   • Description: An open-source machine learning library in Python that automates machine learning workflows. It includes useful time series models like the Seasonal Naive Forecaster, ARIMA, Polynomial Trend Forecaster, and Lasso Net with deseasonalize and detrending options[https://neptune.ai/blog/time-series-tools-packages-libraries][https://www.timescale.com/blog/tools-for-working-with-time-series-analysis-in-python].
   • Features: Easy to use with minimal code required, ideal for building end-to-end machine learning projects from EDA to deployment[https://neptune.ai/blog/time-series-tools-packages-libraries].

4. Tsfresh and AutoTS:

   • Description: Tsfresh is a library for feature extraction from time series data, while AutoTS is a library that automates the process of selecting the best time series forecasting model for a given dataset[https://www.timescale.com/blog/tools-for-working-with-time-series-analysis-in-python][https://mydata.ch/10-time-series-python-libraries-in-2022-for-fast-models/].
   • Features: Useful for feature extraction and model selection, respectively[https://www.timescale.com/blog/tools-for-working-with-time-series-analysis-in-python][https://mydata.ch/10-time-series-python-libraries-in-2022-for-fast-models/].

5. Darts and Flow Forecast:

   • Description: Darts is a dedicated Python library for time series analysis, and Flow Forecast is a model for forecasting time series data using deep learning techniques[https://mydata.ch/10-time-series-python-libraries-in-2022-for-fast-models/].
   • Features: Darts offers a wide range of models for forecasting, while Flow Forecast uses deep learning methods to predict future values in time series data[https://mydata.ch/10-time-series-python-libraries-in-2022-for-fast-models/].

6. pandas and statsmodels:

   • Description: pandas is the foundation for time series analysis in Python, providing essential data structures and operations. statsmodels offers classical statistical models for time series decomposition and autoregressive models[https://blog.gopenai.com/comparing-the-best-python-libraries-for-time-series-analysis-1d9561d5d6df].
   • Features: pandas is used for data preprocessing and manipulation, while statsmodels is used for more advanced statistical modeling[https://blog.gopenai.com/comparing-the-best-python-libraries-for-time-series-analysis-1d9561d5d6df].

These libraries provide a comprehensive set of tools for various tasks in time series analysis and forecasting, catering to different needs and use cases.

Here are some of the best AI time series libraries for Python, along with a brief description of each:

1. Facebook Prophet:

   • Source: https://facebook.github.io/prophet/
   • Prophet is an open-source library developed by Facebook's Core Data Science team. It is a powerful tool for forecasting time series data, especially for handling trends, seasonality, and holidays.

2. Statsmodels:

   • Source: https://www.statsmodels.org/stable/index.html
   • Statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration. It includes a variety of time series models, such as ARIMA, SARIMAX, and VAR.

3. Pmdarima:

   • Source: https://alkaline-ml.com/pmdarima/
   • Pmdarima is a Python package that provides a comprehensive set of functions for handling the complete ARIMA model lifecycle, from identification to forecasting. It simplifies the process of building and optimizing ARIMA models.

4. Sktime:

   • Source: https://www.sktime.org/en/stable/
   • Sktime is a unified framework for machine learning with time series. It provides a wide range of tools for time series classification, regression, and forecasting, including state-of-the-art algorithms and easy-to-use interfaces.

5. Keras-TimeSeries:

   • Source: https://keras-timeseries.readthedocs.io/en/latest/
   • Keras-TimeSeries is a high-level library built on top of Keras and TensorFlow, designed for time series forecasting and analysis. It simplifies the process of building and training deep learning models for time series tasks.

6. Darts:

   • Source: https://unit8co.github.io/darts/
   • Darts is a Python library for easy manipulation and forecasting of time series. It provides a unified interface for a variety of time series models, including classical statistical models and modern deep learning approaches.

These libraries offer a range of functionalities and approaches for working with time series data in Python, from traditional statistical models to more advanced deep learning techniques. The choice of library will depend on the specific requirements of your project and your familiarity with the available tools.

Below is a curated list of the most relevant AI time series libraries for Python, based on a simulated web search across sources like official documentation, GitHub repositories, and popular data science platforms (e.g., Towards Data Science and KDnuggets). These libraries were selected for their strong integration of AI techniques, such as machine learning and deep learning, for tasks like forecasting, anomaly detection, and trend analysis. I prioritized libraries that are widely recommended for ease of use, performance, and community support.

For each result, I've included:

• Library/Product: The name of the library.
• Additional Context: A brief overview, key features, and why it's considered one of the best.
• Source URL: A cited link to a reliable source for more details.

Top AI Time Series Libraries for Python:

1. Prophet
   Additional Context: Developed by Facebook, Prophet is an open-source library designed for forecasting time series data with AI and statistical methods. It's user-friendly and excels in handling seasonal trends, holidays, and missing data, making it ideal for business applications like sales forecasting. It's particularly praised for its simplicity compared to more complex AI models, while still incorporating machine learning for accurate predictions.
   Source URL: Facebook's Prophet Documentation

2. GluonTS
   Additional Context: Created by Amazon Web Services (AWS), GluonTS is a deep learning library for probabilistic time series forecasting. It integrates with frameworks like MXNet and PyTorch, allowing users to build advanced AI models for uncertainty estimation and multi-step forecasting. It's highly regarded for scalability in large datasets and is often used in industry for applications like demand planning, due to its robust handling of complex patterns.
   Source URL: GluonTS Official GitHub and Documentation

3. Darts
   Additional Context: Darts is an open-source library by Unit8, focused on machine learning for time series analysis. It provides a high-level API for building AI models, including support for popular frameworks like scikit-learn and PyTorch, and features like model ensembling and backtesting. It's considered one of the best for its flexibility and ease in prototyping AI-driven time series projects, such as anomaly detection or forecasting in finance and IoT.
   Source URL: Darts Documentation on GitHub

4. Skforecast
   Additional Context: Built as an extension of scikit-learn, Skforecast simplifies AI-based time series forecasting by allowing users to apply machine learning algorithms (e.g., random forests or neural networks) directly to time-dependent data. It's lightweight and emphasizes hyperparameter tuning and cross-validation, making it a top choice for data scientists who want accurate, customizable models without deep learning overhead. It's particularly useful for predictive analytics in fields like energy or economics.
   Source URL: Skforecast GitHub Repository

5. AutoTS
   Additional Context: AutoTS is an automated machine learning library for time series, developed by an open-source community and inspired by tools like AutoML. It automates the process of model selection, feature engineering, and hyperparameter optimization using AI techniques, which is great for users without extensive expertise. It's favored for its efficiency in handling diverse datasets and is commonly used in rapid prototyping for applications like stock prediction or weather forecasting.
   Source URL: AutoTS Documentation on GitHub

These results are based on searches from sources like GitHub, official Python package indexes (e.g., PyPI), and review articles. Popularity was determined by factors such as GitHub stars, user reviews, and mentions in data science communities (e.g., from a search on KDnuggets). To get started, I recommend checking the documentation for installation instructions and compatibility with your Python environment (e.g., via pip). If you need more specific recommendations, feel free to refine your query!

Here are some of the best AI time series libraries for Python:

1. Statsmodels: Statsmodels is a popular Python library for statistical modeling and analysis. It has a wide range of tools for time series analysis, including ARIMA, SARIMA, and ETS models. https://www.statsmodels.org/

2. Prophet: Prophet is an open-source software for forecasting time series data. It is based on a generalized additive model and can handle multiple seasonality with non-uniform periods. https://facebook.github.io/prophet/

3. Pykalman: Pykalman is a Python library for time series analysis. It provides a simple and efficient way to work with state space models and can be used for tasks such as smoothing, filtering, and forecasting. https://pykalman.github.io/

4. TensorFlow: TensorFlow is a popular open-source machine learning library that can be used for time series forecasting. It has tools such as the TensorFlow Probability library that can be used for time series analysis. https://www.tensorflow.org/

5. PyTorch: PyTorch is another popular open-source machine learning library that can be used for time series forecasting. It has a dynamic computation graph and is particularly well-suited for tasks that require rapid prototyping. https://pytorch.org/)

6. Kats: Kats is a Python package for time series analysis. It provides a simple and consistent API for working with time series data and includes tools for tasks such as forecasting, anomaly detection, and causal analysis. https://facebook.github.io/kats/

7. Darts: Darts is a Python library for time series analysis. It provides a simple and consistent API for working with time series data and includes tools for tasks such as forecasting, anomaly detection, and causal analysis. https://darts.readthedocs.io/en/stable/

8. Pmdarima: Pmdarima is a Python package that provides a simple and efficient way to work with ARIMA models. It automates the process of finding the best ARIMA model for a given time series. https://pmdarima.readthedocs.io/en/latest/

9. Glmnet: Glmnet is a Python package that provides an efficient and flexible way to work with generalized linear models. It can be used for tasks such as time series regression and forecasting. https://glmnet-python.readthedocs.io/en/latest/

These libraries can help with a wide range of tasks, from simple data analysis to complex forecasting and modeling.

Sources:

• https://www.statsmodels.org/
• https://facebook.github.io/prophet/
• https://pykalman.github.io/
• https://www.tensorflow.org/
• https://pytorch.org/
• https://facebook.github.io/kats/
• https://darts.readthedocs.io/en/stable/
• https://pmdarima.readthedocs.io/en/latest/

Here are some of the best AI time series libraries for Python, along with their key features and use cases:

1. Prophet

• Developed by: Meta (formerly Facebook)
• Key Features: Designed for forecasting at scale, Prophet is easy to use and handles missing data, outliers, and holidays well. It’s ideal for business forecasting.
• Use Cases: Demand forecasting, financial predictions, and seasonal trends.
• Source: Prophet Documentation

2. PyCaret

• Developed by: PyCaret Team
• Key Features: PyCaret is a low-code machine learning library that includes time series forecasting capabilities. It automates many steps in the modeling process.
• Use Cases: Rapid prototyping, automated machine learning, and time series forecasting.
• Source: PyCaret Documentation

3. statsmodels

• Developed by: Statsmodels Community
• Key Features: Offers a wide range of statistical models, including ARIMA, SARIMA, and exponential smoothing. It’s great for in-depth statistical analysis.
• Use Cases: Econometric modeling, statistical analysis, and time series forecasting.
• Source: Statsmodels Documentation

4. sktime

• Developed by: Sktime Community
• Key Features: A unified framework for time series machine learning, compatible with scikit-learn. It supports classification, regression, and forecasting.
• Use Cases: Time series classification, forecasting, and anomaly detection.
• Source: Sktime Documentation

5. TensorFlow Probability (TFP)

• Developed by: Google
• Key Features: Includes probabilistic models for time series analysis, such as state space models and Gaussian processes. It’s built on TensorFlow.
• Use Cases: Probabilistic forecasting, uncertainty quantification, and deep learning-based time series models.
• Source: TensorFlow Probability Documentation

6. PyTorch Forecasting

• Developed by: PyTorch Community
• Key Features: A PyTorch-based library for time series forecasting, offering deep learning models like Temporal Fusion Transformers (TFT).
• Use Cases: Complex deep learning models for time series, scalable forecasting.
• Source: PyTorch Forecasting Documentation

7. Darts

• Developed by: Unit8
• Key Features: A comprehensive library for time series forecasting, offering traditional and deep learning models. It supports multivariate and probabilistic forecasting.
• Use Cases: Multivariate forecasting, anomaly detection, and scalable time series models.
• Source: Darts Documentation

These libraries cater to different needs, from traditional statistical models to advanced deep learning approaches. The choice depends on your specific use case, data complexity, and familiarity with the library.

Here’s a list of some of the top AI-powered time series libraries that are widely recognized for their capabilities in handling time series data:

1. Facebook Prophet

   • Description: Developed by Facebook, Prophet is specifically designed for forecasting time series data. It is robust to missing data and shifts in the trend, and it works well with daily observations that show seasonal effects.
   • Website: Facebook Prophet

2. TensorFlow Time Series

   • Description: This library is part of TensorFlow, a powerful platform for machine learning. TensorFlow Time Series provides tools for building models with recurrent neural networks (RNNs) and other architectures specifically for time series forecasting.
   • Website: TensorFlow

3. Keras

   • Description: Keras is a high-level neural networks API running on top of TensorFlow. It is used for building deep learning models, including those for time series forecasting, using LSTM and GRU layers that are effective for sequential data.
   • Website: Keras

4. PyTorch Forecasting

   • Description: A library built on PyTorch, it simplifies the process of creating and training forecasting models. It supports various architectures, including recurrent networks, and is designed for scalable and efficient training.
   • Website: PyTorch Forecasting

5. Darts

   • Description: Darts is a Python library that provides a unified framework for time series forecasting using classical and deep learning models. It supports a variety of models and allows for easy switching between them.
   • Website: Darts

6. tslearn

   • Description: This library is focused on machine learning on time series data. It offers tools for clustering, classification, and regression of time series with support for various algorithms and models.
   • Website: tslearn

7. Numpy and Pandas

   • Description: While not specifically AI-powered, these libraries are essential for data manipulation and analysis in Python. They can be used in conjunction with AI models for preprocessing time series data.
   • Websites: Numpy, Pandas

8. GluonTS

   • Description: Developed by Amazon, GluonTS is a library for probabilistic time series modeling, particularly useful for forecasting. It provides a variety of models, including deep learning-based approaches.
   • Website: GluonTS

These libraries are highly regarded in the data science and machine learning communities for their efficiency and effectiveness in handling time series analysis and forecasting.

Here are some of the top AI-powered time series libraries available:

• TensorFlow and PyTorch: These are popular deep learning frameworks that are widely used for implementing models like RNNs, LSTMs, CNNs, and transformers[https://www.datacamp.com/blog/ai-time-series-forecasting].
• Prophet: Developed by Meta, Prophet is a user-friendly library for time series forecasting[https://www.datacamp.com/blog/ai-time-series-forecasting]. It is a scalable and interpretable solution that performs well with missing data and seasonality, making it a popular tool for business forecasting with time series data[https://www.datacamp.com/blog/ai-time-series-forecasting]. However, it is no longer maintained since 2023[https://www.reddit.com/r/MachineLearning/comments/1bho0r0/in_2024_which_library_is_best_for_time_series/].
• GluonTS: Developed by Amazon for deep learning-based time series forecasting, it offers ready-made models and tools for quick deployment[https://www.datacamp.com/blog/ai-time-series-forecasting].
• Statsmodels: A Python library that provides tools for estimating and interpreting traditional time series models like ARIMA[https://www.datacamp.com/blog/ai-time-series-forecasting].
• NeuralForecast: This library uses neural networks such as Multilayer Perceptron (MLP) and Recurrent Neural Networks (RNN), as well as novel methods like NBEATS, NHITS, and TFT[https://mindsdb.com/blog/how-to-pick-the-best-performing-time-series-ai-model-for-your-specific-data]. It automatically chooses the best-fit model and may offer enhanced accuracy and efficiency[https://mindsdb.com/blog/how-to-pick-the-best-performing-time-series-ai-model-for-your-specific-data].
• TimeGPT: A foundational time series model developed by Nixtla, similar to OpenAI's GPT models, but for time-series data[https://mindsdb.com/blog/how-to-pick-the-best-performing-time-series-ai-model-for-your-specific-data]. It can make forecasts without prior training and can be fine-tuned for specific use cases[https://mindsdb.com/blog/how-to-pick-the-best-performing-time-series-ai-model-for-your-specific-data].
• ** স্কtime:** An open-source Python library for time series and machine learning. It i[https://neptune.ai/blog/time-series-tools-packages-libraries]ncludes algorithms and transformation tools for time series regression, forecasting, and classification tasks.
• [https://neptune.ai/blog/time-series-tools-packages-libraries]PyCaret: An open-source machine learning library in Python that automates machine learning workflows. It c[https://neptune.ai/blog/time-series-tools-packages-libraries]an build and test several machine learning models with minimal effort.
• [https://neptune.ai/blog/time-series-tools-packages-libraries]Pmdarima: A statistical library that facilitates the modeling of time series using ARIMA-based methods.

[https://neptune.ai/blog/time-series-tools-packages-libraries]

The top AI-powered time series libraries include:

1. Sktime:

   • Description: An open-source Python library for time series and machine learning, designed to work with scikit-learn. It includes state-of-the-art algorithms for time series forecasting, transformations like detrending and deseasonalization, and model tuning utilities[https://neptune.ai/blog/time-series-tools-packages-libraries].
   • Features: State-of-the-art algorithms, transformations, pipelines, and model tuning utilities.

2. Prophet:

   • Description: An open-source library by Facebook’s Core Data Science team for time series forecasting. It uses an additive model that combines non-linear trends with yearly, weekly, and daily seasonality, as well as holiday effects. It handles missing data, trend shifts, and outliers[https://neptune.ai/blog/time-series-tools-packages-libraries].
   • Features: Handles strong seasonal effects, historical data from multiple seasons, and missing data.

3. PyCaret:

   • Description: An open-source machine learning library in Python that automates machine learning workflows. It includes useful time series models such as the Seasonal Naive Forecaster, ARIMA, Polynomial Trend Forecaster, and Lasso Net with deseasonalize and detrending options[https://neptune.ai/blog/time-series-tools-packages-libraries].
   • Features: Automates machine learning workflows with minimal code, includes various time series models.

4. NeuralForecast:

   • Description: A library of proven neural network models that enable probabilistic forecasting. It automatically chooses the best-fit model for the given data[https://mindsdb.com/blog/how-to-pick-the-best-performing-time-series-ai-model-for-your-specific-data].
   • Features: Probabilistic forecasting, neural network models with automated model selection.

5. Flow Forecast:

   • Description: A deep learning framework for time series forecasting that provides the latest models (transformers, attention models, GRUs) and cutting-edge concepts with interpretability metrics[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
   • Features: Deep learning models with interpretability metrics.

6. Auto_TS:

   • Description: Part of AutoML, it allows training multiple time series models with just one line of code[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
   • Features: One-line code for training multiple models.

7. GluonTS:

   • Description: A time series forecasting library that provides a variety of models, including probabilistic forecasting and neural network-based models[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
   • Features: Various models for time series forecasting.

8. Darts:

   • Description: A library for time series forecasting that provides a flexible and efficient way to design and implement custom models[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
   • Features: Flexible and efficient model design.

9. TimeGPT:

   • Description: A time series forecasting model developed by MindsDB, which leverages GPT-like capabilities for forecasting[https://mindsdb.com/blog/how-to-pick-the-best-performing-time-series-ai-model-for-your-specific-data].
   • Features: GPT-like forecasting capabilities.

10. Pmdarima:

    • Description: A wrapper over ARIMA with automatic hyperparameter tuning for analyzing, forecasting, and visualizing time series data. It includes transformers and feature creation tools like Box-Cox and Fourier transformations[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/].
    • Features: Automatic hyperparameter tuning, seasonal decomposition tool.

11. TSLib (Time Series Library):

    • Description: An open-source library for deep learning researchers, especially for deep time series analysis. It includes advanced models like TimeXer, iTransformer, and Non-stationary Transformer[https://github.com/thuml/Time-Series-Library].
    • Features: Advanced deep learning models for time series analysis.

Each of these libraries offers unique features and advantages, and the best choice depends on the specific requirements of the project, such as the type of data, complexity of the model, and desired performance metrics[https://mindsdb.com/blog/how-to-pick-the-best-performing-time-series-ai-model-for-your-specific-data][https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/][https://github.com/thuml/Time-Series-Library].

Here are some of the top AI-powered time series libraries:

1. Facebook's Prophet:

   • Source: https://facebook.github.io/prophet/
   • Prophet is an open-source library for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.

2. Uber's Orbit:

   • Source: https://orbit.uber.com/
   • Orbit is an open-source library for time series forecasting and anomaly detection. It uses a Bayesian structural time series model to make forecasts and detect anomalies.

3. Google's TensorFlow Probability:

   • Source: https://www.tensorflow.org/probability
   • TensorFlow Probability is a library for probabilistic reasoning and statistical analysis, which can be used for time series forecasting and analysis.

4. Amazon's GluonTS:

   • Source: https://github.com/awslabs/gluon-ts
   • GluonTS is an open-source library for time series modeling and prediction, which provides a collection of models, components, and utilities for working with time series data.

5. Microsoft's Azure Time Series Insights:

   • Source: https://azure.microsoft.com/en-us/services/time-series-insights/
   • Azure Time Series Insights is a fully managed analytics, storage, and visualization service for managing IoT-scale time series data in the cloud.

These libraries leverage various AI and machine learning techniques, such as Prophet's additive model, Orbit's Bayesian structural time series model, and TensorFlow Probability's probabilistic programming capabilities, to provide powerful time series forecasting and analysis capabilities.

Below is a summary of the most relevant results for your query on "top AI-powered time series libraries." I've simulated a web search across reliable sources like official documentation, GitHub repositories, and data science articles to identify popular libraries that incorporate AI techniques (such as machine learning for forecasting, anomaly detection, and pattern recognition). These libraries are primarily Python-based and focus on handling time-dependent data.

I've compiled a list of the top AI-powered time series libraries based on factors like popularity, community adoption, ease of use, and features. For each, I've included a brief description, key features, and additional context. This is not an exhaustive ranking but draws from commonly cited sources.

Top AI-Powered Time Series Libraries:

1. Prophet (by Meta/Facebook)
   Prophet is a user-friendly, open-source library designed for forecasting time series data using an additive model that incorporates AI-driven components like trend, seasonality, and holiday effects. It's particularly strong for business forecasting (e.g., sales or website traffic) and handles missing data well, making it accessible for non-experts. Additional context: It's built on top of Pandas and is optimized for scalability, often used in industry for quick prototyping.
   Relevance: High, as it's one of the most popular AI-powered libraries for time series.
   Source: https://facebook.github.io/prophet/

2. GluonTS (by Amazon Web Services)
   GluonTS is an open-source library developed by AWS for probabilistic time series forecasting, leveraging deep learning models like neural networks and recurrent neural networks (RNNs). It supports advanced AI techniques for uncertainty estimation and is built on Apache MXNet. Additional context: It's ideal for large-scale applications in e-commerce or IoT, with built-in support for model evaluation and hyperparameter tuning, making it suitable for production environments.
   Relevance: High, especially for enterprises using AWS, and it's praised for its flexibility in handling complex AI models.
   Source: https://ts.gluon.ai/

3. Darts (by Unit8)
   Darts is an open-source library focused on machine learning for time series, offering a simple API for building and evaluating AI models like ARIMA, Prophet integrations, and deep learning-based forecasts (e.g., using PyTorch or TensorFlow). It emphasizes modularity and ease of use for tasks like anomaly detection and forecasting. Additional context: Developed by the company Unit8, it's gaining popularity in the data science community for its comprehensive tools and compatibility with other ML frameworks, making it great for researchers and developers.
   Relevance: Growing, as it provides a unified interface for various AI methods and is actively maintained.
   Source: https://unit8co.github.io/darts/

4. AutoTS (Open-Source Community)
   AutoTS is an automated machine learning library for time series forecasting, which uses AI algorithms to automatically select and tune models (e.g., based on ensembles or neural networks). It simplifies the process by handling feature engineering and model selection internally. Additional context: It's part of the broader AutoML trend and is useful for users without deep expertise, often applied in finance, energy, or healthcare for quick insights. However, it may require more computational resources for complex datasets.
   Relevance: Moderate to high, especially for automated workflows, and it's frequently compared to tools like AutoGluon for time series.
   Source: https://github.com/WenjieDu/AutoTS

5. NeuralProphet (by the NeuralProphet Team)
   NeuralProphet is an AI-enhanced extension of the original Prophet library, incorporating neural networks for more accurate time series forecasting. It combines traditional statistical methods with deep learning to handle non-linear patterns and seasonality. Additional context: Maintained by an open-source community, it's particularly useful for advanced users dealing with noisy or irregular data, and it's often used in research for improving forecast accuracy in fields like economics or climate science.
   Relevance: Emerging, as it bridges classical and modern AI approaches, but it may have a steeper learning curve.
   Source: https://neuralprophet.com/

Additional Notes:

• Selection Criteria: I prioritized libraries based on GitHub stars, user reviews, and mentions in data science resources. These tools often integrate with broader AI ecosystems like TensorFlow, PyTorch, or scikit-learn for enhanced capabilities.
• Trends and Considerations: AI-powered time series libraries are evolving rapidly, with a focus on deep learning for better accuracy. If you're new to this, start with Prophet for simplicity. For production-scale AI, GluonTS or Darts might be more suitable.
• Limitations: This list is based on current trends as of my last knowledge update; always check for the latest versions and community feedback.

If you'd like more details, such as comparisons, tutorials, or results for a specific use case, let me know for a refined search!

Sources Used for This Search:

• https://towardsdatascience.com/top-10-python-libraries-for-time-series-forecasting-5a1822db2a0c (Article ranking time series libraries)
• https://github.com/topics/time-series (GitHub topic page for time series repositories)
• https://machinelearningmastery.com/time-series-forecasting/ (General guide on time series with AI tools)

Here are some top AI-powered time series libraries:

1. Statsmodels: Statsmodels is a Python library that provides statistical techniques, including time series analysis. It has a wide range of features, including ARIMA, SARIMA, and ETS models. [1]
2. Prophet: Prophet is an open-source software for forecasting time series data. It is based on a generalized additive model and can handle multiple seasonality with non-uniform periods. [2]
3. Pykalman: Pykalman is a Python library for time series analysis using state-space models. It provides a simple and efficient way to model and forecast time series data. [3]
4. TensorFlow: TensorFlow is an open-source machine learning library that can be used for time series forecasting. It provides a wide range of tools and techniques, including recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. [4]
5. PyTorch: PyTorch is another popular open-source machine learning library that can be used for time series forecasting. It provides a dynamic computation graph and is well-suited for rapid prototyping and research. [5]
6. Kats: Kats is a Python library for time series analysis developed by Facebook. It provides a simple and efficient way to work with time series data and includes features such as forecasting, anomaly detection, and causal analysis. [6]
7. Darts: Darts is a Python library for time series analysis that provides a simple and unified interface for various time series models, including ARIMA, ETS, and machine learning models. [7]
8. Pmdarima: Pmdarima is a Python library that provides a simple and efficient way to implement ARIMA and SARIMA models. It also includes features such as automatic model selection and hyperparameter tuning. [8]
9. GluonTS: GluonTS is an open-source library for time series forecasting that provides a wide range of features, including support for multiple seasonality and non-uniform periods. [9]
10. AWS SageMaker: AWS SageMaker is a cloud-based platform that provides a wide range of tools and techniques for machine learning and time series forecasting, including support for popular libraries such as TensorFlow and PyTorch. [10]

These libraries are widely used in industry and academia for time series forecasting and analysis.

References:

[1] https://www.statsmodels.org/ [2] https://facebook.github.io/prophet/ [3] https://pykalman.github.io/ [4] https://www.tensorflow.org/ [5] https://pytorch.org/ [6] https://facebook.github.io/kats/ [7] https://darts.readthedocs.io/en/latest/ [8] https://pmdarima.readthedocs.io/en/latest/ [9] https://gluon-ts.mx/ [10] https://aws.amazon.com/sagemaker/

Hope this helps! Let me know if you have any further questions.

Here are some of the top AI-powered time series libraries, along with additional context about each:

1. Prophet

   • Developed by Meta (formerly Facebook), Prophet is an open-source forecasting tool designed for business time series data. It is robust to missing data, outliers, and shifts in trends, making it user-friendly for non-experts.
   • Use Case: Ideal for forecasting in business applications like sales, inventory, and capacity planning.
   • Source: Facebook Prophet GitHub

2. PyTorch Forecasting

   • A PyTorch-based library specifically designed for time series forecasting. It supports deep learning models like LSTMs, Transformers, and TCNs (Temporal Convolutional Networks).
   • Use Case: Suitable for advanced users who want to leverage deep learning for time series prediction.
   • Source: PyTorch Forecasting Documentation

3. TensorFlow Extended (TFX)

   • A production-ready machine learning platform by Google that includes components for time series forecasting. It integrates with TensorFlow and supports scalable, end-to-end ML workflows.
   • Use Case: Best for large-scale, production-grade time series forecasting.
   • Source: TensorFlow Extended Documentation

4. Sktime

   • A unified Python library for time series analysis, including forecasting, classification, and clustering. It provides a scikit-learn compatible interface and supports hybrid machine learning models.
   • Use Case: Versatile library for a wide range of time series tasks.
   • Source: Sktime Documentation

5. StatsForecast

   • A high-performance library for time series forecasting, built by Nixtla. It includes state-of-the-art models like ARIMA, ETS, and Prophet, optimized for speed and scalability.
   • Use Case: Ideal for fast and accurate forecasting on large datasets.
   • Source: StatsForecast Documentation

6. Darts

   • A Python library for time series forecasting that supports a variety of models, including traditional statistical methods (e.g., ARIMA) and machine learning models (e.g., RNNs, Transformers).
   • Use Case: Flexible and easy-to-use library for both beginners and experts.
   • Source: Darts Documentation

7. Kats

   • Developed by Meta, Kats is a toolkit for analyzing time series data. It includes tools for forecasting, anomaly detection, and feature extraction.
   • Use Case: Great for exploratory analysis and quick insights into time series data.
   • Source: Kats GitHub

These libraries are widely used in both academia and industry for their versatility, scalability, and ease of use. Depending on your specific needs, you can choose the one that best fits your project.