Here are some of the most popular time series libraries in Python as of 2023:

1. Pandas:

   • Context: Pandas is one of the most widely used libraries for data manipulation and analysis in Python. It provides robust functionality for handling time series data, including date range generation, frequency conversion, and resampling.
   • Website: Pandas

2. Statsmodels:

   • Context: Statsmodels is a library that provides classes and functions for estimating and testing statistical models. It has extensive capabilities for handling time series data, including ARIMA, SARIMA, and state space models.
   • Website: Statsmodels

3. Prophet:

   • Context: Developed by Facebook, Prophet is designed for forecasting time series data that may contain seasonal effects. It is user-friendly and particularly useful for business applications.
   • Website: Prophet

4. Darts:

   • Context: Darts is a relatively newer library that simplifies forecasting with time series data. It supports various forecasting models, including ARIMA, Exponential Smoothing, and machine learning models, and provides an easy-to-use interface.
   • Website: Darts

5. TensorFlow/Keras:

   • Context: While primarily known as a deep learning framework, TensorFlow (and its high-level API Keras) has capabilities for building recurrent neural networks (RNNs) and models like LSTM (Long Short-Term Memory) for time series forecasting.
   • Website: TensorFlow

6. Kats:

   • Context: Developed by Facebook, Kats is a toolkit for analyzing time series data. It supports various tasks including forecasting, anomaly detection, and feature extraction.
   • Website: Kats

7. PyTorch:

   • Context: Like TensorFlow, PyTorch is a deep learning library that can be used for time series analysis, particularly with RNNs and LSTMs. It's favored for its dynamic computation graph and ease of use.
   • Website: PyTorch

8. tsfresh:

   • Context: tsfresh is a library specifically designed for time series feature extraction. It automatically calculates a large number of time series characteristics and is useful for machine learning tasks.
   • Website: tsfresh

These libraries are widely recognized for their capabilities in time series analysis and forecasting, catering to a variety of needs from basic data manipulation to advanced modeling techniques.

It appears you're asking about popular Python libraries used for time series analysis. Here's a breakdown of some of the most frequently mentioned and useful ones:

1. Foundational Libraries:

• NumPy: This is the base for numerical computing in Python, offering tools for working with arrays and matrices, which are essential for handling time series data.[https://dev.to/taipy/python-libraries-you-need-to-know-in-2024-37ka][https://www.timescale.com/blog/tools-for-working-with-time-series-analysis-in-python]
• pandas: This library provides data structures (like DataFrames) and tools specifically designed for data manipulation and analysis, including robust time series capabilities.[https://neptune.ai/blog/time-series-tools-packages-libraries][https://www.timescale.com/blog/how-to-work-with-time-series-in-python][https://dev.to/taipy/python-libraries-you-need-to-know-in-2024-37ka][https://www.geeksforgeeks.org/python-libraries-to-know/] It's excellent for loading, cleaning, and transforming time series data.
• Matplotlib: A widely used plotting library for creating visualizations of your data, including time series plots (line charts, scatter plots, histograms, etc.).[https://www.timescale.com/blog/how-to-work-with-time-series-in-python][https://www.timescale.com/blog/tools-for-working-with-time-series-analysis-in-python]

2. Core Time Series Libraries:

• statsmodels: This library offers statistical models, including time series models like ARIMA, and tools for analysis and forecasting.
• scikit-learn (sklearn): A general machine learning library that also provides tools for time series tasks, including model evaluation, preprocessing, and building pipelines.
• sktime: A dedicated machine learning library for time series data.[https://www.timescale.com/blog/tools-for-working-with-time-series-analysis-in-python][https://www.timescale.com/blog/how-to-work-with-time-series-in-python] It's designed to work with scikit-learn and provides algorithms and tools for time series regression, forecasting, and classification.[https://neptune.ai/blog/time-series-tools-packages-libraries]
• Prophet: Specifically designed for forecasting time series data, particularly data with seasonality and holiday effects. Developed by Facebook.[https://www.timescale.com/blog/tools-for-working-with-time-series-analysis-in-python][https://www.analyticsvidhya.com/blog/2022/05/5-python-libraries-for-time-series-analysis/]

3.[https://www.geeksforgeeks.org/python-libraries-to-know/][https://www.analyticsvidhya.com/blog/2022/05/5-python-libraries-for-time-series-analysis/] Advanced & Specialized Libraries:

• AutoTS: An automated time series framework that helps you find the best model for your data.[https://www.timescale.com/blog/tools-for-working-with-time-series-analysis-in-python]
• Tsfresh: This library automatically extracts a large number of features from time series data.[https://neptune.ai/blog/time-series-tools-packages-libraries][https://www.timescale.com/blog/tools-for-working-with-time-series-analysis-in-python]
• Darts: A library that makes forecasting with various models (from ARIMA to deep learning) easier.[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/][https://github.com/MaxBenChrist/awesome_time_series_in_python] It also supports ensembling and model selection.[https://github.com/MaxBenChrist/awesome_time_series_in_python]
• GluonTS: A library focused on probabilistic time series modeling, especially using deep learning models.[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.[https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]
• PyCaret: A low-code machine learning library that automates machine learning workflows, including time series analysis.[https://neptune.ai/blog/time-series-tools-packages-libraries]
• Flow Forecast: A deep learning framework for time series forecasting, classification, and anomaly detection.[https://github.com/MaxBenChrist/awesome_time_series_in_python]

Important Considerations:

• Visualization: Remember that visualizing time series data is crucial for understanding patterns and trends.[https://www.timescale.com/blog/how-to-work-with-time-series-in-python] Matplotlib is a good starting point, but libraries like Seaborn and Plotly offer more advanced visualization options.
• Feature Extraction: Libraries like Tsfresh can automatically extract features from time series data, which can be useful for machine learning models.[https://neptune.ai/blog/time-series-tools-packages-libraries][https://www.timescale.com/blog/tools-for-working-with-time-series-analysis-in-python][https://www.timescale.com/blog/how-to-work-with-time-series-in-python]
• Deep Learning: For more complex time series problems, consider libraries like TensorFlow, PyTorch, and specialized time series libraries that incorporate deep learning models (e.g., GluonTS, NeuralForecast).

The most popular time series libraries in Python currently include:

1. Darts: A library that makes it easy to produce forecasts using a wide range of models, from ARIMA to deep learning. It also supports ensembling, model selection, and more[https://github.com/MaxBenChrist/awesome_time_series_in_python].

2. ETNA: A Python library for time series forecasting and analysis with a focus on temporal data structures. It includes a variety of predictive models with a unified interface along with EDA (Exploratory Data Analysis) and validation methods[https://github.com/MaxBenChrist/awesome_time_series_in_python].

3. Kats: An open-source Python library for time series analysis and forecasting. It supports various techniques such as ARIMA, SARIMAX, VAR, and ensemble machine learning models.

4. AutoTS: Enables the building and selection of multiple time series models using techniques like ARIMA, SARIMAX, VAR, decomposable (trend+seasonality+holidays) models, and ensemble machine learning models.

5. Pycaret: Although primarily known for its general machine learning capabilities, it can also be used for time series forecasting by integrating various models.

6. Tsfresh: A package that automatically calculates a large number of time series characteristics, known as features. It combines established algorithms from statistics, time series analysis, signal processing, and non-linear dynamics with a robust feature selection algorithm[https://github.com/MaxBenChrist/awesome_time_series_in_python][https://neptune.ai/blog/time-series-tools-packages-libraries].

7. GluonTS: A library for probabilistic time series forecasting. It supports a variety of models including Gaussian processes and neural networks[https://blog.gopenai.com/python-libraries-for-time-series-forecasting-3e8e92e03812].

These libraries are widely used in the field of time series analysis and forecasting due to their robust features and ease of implementation.

Additional Context

• Bayesian Forecasting: Libraries like Orbit support Bayesian time series forecasting and inference, which can be particularly useful for modeling uncertainty in time series data[https://github.com/MaxBenChrist/awesome_time_series_in_python].
• Deep Learning Libraries: PyTorch Forecasting and Keras (for LSTM/GRU) are also popular choices for time series forecasting, especially when deep learning models are required[https://blog.gopenai.com/python-libraries-for-time-series-forecasting-3e8e92e03812].
• Data Visualization: Matplotlib is often used in conjunction with these libraries for data visualization purposes[https://neptune.ai/blog/time-series-tools-packages-libraries].

Based on my web search, here are some of the most popular time series libraries in Python currently:

1. Pandas:

   • Pandas is a powerful data manipulation and analysis library in Python, which includes robust time series functionality.
   • It provides data structures and data analysis tools for working with time-indexed data, including support for time zone handling, resampling, and time-based selection.

2. NumPy:

   • NumPy is a fundamental library for scientific computing in Python, which includes support for working with time series data.
   • It provides efficient arrays and functions for working with numerical data, including time-based operations and calculations.

3. Matplotlib:

   • Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
   • It is commonly used in conjunction with Pandas and other time series libraries to create high-quality plots and visualizations of time series data.

4. Statsmodels:

   • 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 and functions, such as ARIMA, VAR, and Kalman filtering.

5. Prophet:

   • Prophet is a forecasting library developed by Facebook's Core Data Science team.
   • It is designed to make it easy to fit and tune time series forecasting models, particularly for business time series data that has multiple seasonal effects and other complex patterns.

6. Darts:

   • Darts is a relatively newer time series library in Python, which provides a unified API for working with a variety of time series models and datasets.
   • It includes support for forecasting, anomaly detection, and multivariate time series analysis.

These are some of the most popular and widely-used time series libraries in the Python ecosystem. The choice of library will depend on the specific requirements of your project and the type of time series analysis you need to perform.

Popular Time Series Libraries in Python

Here are some of the most popular time series libraries in Python, along with a brief description and their GitHub stars:

1. Pandas

• GitHub stars: 37.4k
• Description: While not exclusively a time series library, Pandas provides robust support for time series data through its DateTimeIndex and PeriodIndex classes.
• Use cases: Data manipulation, analysis, and visualization.

2. Statsmodels

• GitHub stars: 10.3k
• Description: Statsmodels provides statistical techniques for time series analysis, including ARIMA, SARIMA, and exponential smoothing.
• Use cases: Time series modeling, forecasting, and statistical analysis.

3. PyAlgoTrade

• GitHub stars: 2.6k
• Description: PyAlgoTrade is a backtesting and trading library that supports time series analysis and technical indicators.
• Use cases: Algorithmic trading, backtesting, and strategy evaluation.

4. TA-Lib

• GitHub stars: 1.4k
• Description: TA-Lib is a technical analysis library that provides a wide range of indicators for time series data.
• Use cases: Technical analysis, indicator calculation, and charting.

5. Arch

• GitHub stars: 1.2k
• Description: Arch is a library for volatility modeling and time series analysis, with a focus on GARCH and other volatility models.
• Use cases: Volatility modeling, risk analysis, and financial modeling.

6. Prophet

• GitHub stars: 14.4k
• Description: Prophet is a open-source software for forecasting time series data, based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality.
• Use cases: Time series forecasting, trend analysis, and seasonality modeling.

7. Dask

• GitHub stars: 11.3k
• Description: Dask is a parallel computing library that can handle large time series datasets by breaking them into smaller chunks.
• Use cases: Big data processing, parallel computing, and scalable time series analysis.

8. GluonTS

• GitHub stars: 2.3k
• Description: GluonTS is a Python library for time series analysis and forecasting, providing a wide range of models and tools.
• Use cases: Time series forecasting, modeling, and analysis.

9. Pykalman

• GitHub stars: 1.4k
• Description: Pykalman is a Python library for state-space modeling and time series analysis, with a focus on Kalman filters.
• Use cases: State-space modeling, Kalman filtering, and time series analysis.

10. TensorFlow Probability (TFP)

• GitHub stars: 8.4k
• Description: TFP is a library for probabilistic modeling and time series analysis, providing tools for Bayesian modeling and inference.
• Use cases: Bayesian modeling, probabilistic forecasting, and uncertainty quantification.

These libraries are widely used in various industries, including finance, economics, and data science.

Sources:

• Pandas documentation
• Statsmodels documentation
• PyAlgoTrade documentation
• TA-Lib documentation
• Arch documentation
• Prophet documentation
• Dask documentation
• GluonTS documentation
• Pykalman documentation
• TensorFlow Probability documentation

Here are the top 10 results related to "time series library," along with some context about each:

1. Pandas (Python)

   • Website: Pandas Documentation
   • Context: Pandas is a powerful data manipulation and analysis library for Python, widely used for time series data. It provides data structures like Series and DataFrame, along with numerous functions to manage and analyze time series data effectively.

2. statsmodels (Python)

   • Website: Statsmodels Documentation
   • Context: Statsmodels is a Python library that provides classes and functions for estimating and testing statistical models. It includes tools for time series analysis, such as ARIMA models, seasonal decomposition, and more.

3. tsibble (R)

   • Website: Tsibble Documentation
   • Context: Tsibble is an R package that provides a tidy representation of time series data. It simplifies working with irregular time series and allows for easy manipulation and analysis using the tidyverse principles.

4. forecast (R)

   • Website: Forecast Documentation
   • Context: The forecast package in R is designed for time series forecasting. It includes methods for exponential smoothing, ARIMA modeling, and seasonal decomposition, making it a go-to for statisticians and analysts.

5. Prophet (Python/R)

   • Website: Prophet Documentation
   • Context: Developed by Facebook, Prophet is an open-source forecasting tool that works well with time series data that exhibit seasonal effects. It is available for both Python and R, making it versatile for various users.

6. xts and zoo (R)

   • Website: xts Documentation | zoo Documentation
   • Context: The xts and zoo packages in R are essential for managing and analyzing irregular time series data. They provide functions for time-based indexing and data manipulation.

7. Tidyquant (R)

   • Website: Tidyquant Documentation
   • Context: Tidyquant integrates the tidyverse with quant financial analysis, making it easier to manipulate and analyze financial time series data using tidy principles.

8. Time Series Analysis Library (TSAL)

   • GitHub: TSAL
   • Context: TSAL is a Python library specifically focused on time series analysis and machine learning. It provides easy-to-use functions for preprocessing, modeling, and evaluating time series data.

9. Keras (with Time Series capabilities)

   • Website: Keras Documentation
   • Context: Keras is a high-level neural networks API written in Python. It can be used for time series forecasting by leveraging recurrent neural networks (RNNs) or convolutional neural networks (CNNs) to analyze sequential data.

10. Darts (Python)

    • Website: Darts Documentation
    • Context: Darts is a Python library for easy manipulation and forecasting of time series data. It supports various forecasting models, including traditional statistical models and machine learning algorithms.

These libraries and tools are widely recognized in the data science community for their capabilities in handling time series data across various programming languages.

Here are ten notable Python libraries for time series analysis, each offering unique features and capabilities:

PyCaret
An open-source, low-code machine learning library that simplifies the process of building and deploying time series forecasting models. It offers automated hyperparameter tuning, experiment logging, and cloud deployment capabilities. [https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]

Darts
A Python library designed for easy manipulation and forecasting of time series data. It includes a wide range of models, from traditional statistical methods like ARIMA to advanced deep learning models, and supports both univariate and multivariate time series. [https://analyticsindiamag.com/ai-trends/top-10-python-tools-for-time-series-analysis/]

Flow Forecast
A deep learning framework for time series forecasting that provides state-of-the-art models, including transformers and attention models. It emphasizes interpretability and offers end-to-end solutions for time series forecasting tasks. [https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]

Auto_TS
An AutoML tool that automates the process of training multiple time series models with minimal code. It aims to simplify the model selection and tuning process for time series forecasting. [https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]

Sktime
An extension to scikit-learn, Sktime provides machine learning tools for time series data, including regression, prediction, and classification. It integrates with other libraries like statsmodels and TSFresh, offering a comprehensive suite for time series analysis. [https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]

Pmdarima
A wrapper over ARIMA models that offers automatic hyperparameter tuning for analyzing, forecasting, and visualizing time series data. It includes features like Box-Cox and Fourier transformations and a seasonal decomposition tool. [https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]

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

Pyflux
A library that builds probabilistic models, advantageous for tasks requiring a comprehensive understanding of uncertainty. It treats latent variables as random variables through joint probability distributions. [https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]

Statsforecast
Offers a collection of univariate time series forecasting models, including ADIDA, Croston, and ETS. It is noted for its speed, being significantly faster than other libraries like pmdarima and Prophet. [https://mydata.ch/10-time-series-python-libraries-in-2024-for-fast-models/]

Kats
Developed by Facebook's Infrastructure Data Science team, Kats is a lightweight, easy-to-use library for time series analysis and forecasting. It provides tools for anomaly detection, feature extraction, and multivariate analysis. [https://neptune.ai/blog/time-series-tools-packages-libraries]

These libraries cater to various aspects of time series analysis, from forecasting and feature extraction to anomaly detection and model deployment, providing a comprehensive toolkit for practitioners.

Here are some of the top time series analysis libraries and tools, based on the search results:

1. Python: A versatile programming language with numerous libraries for time series analysis, such as:[https://www.devopsschool.com/blog/top-10-time-series-analysis-tools/][https://paperswithcode.com/task/time-series]

   • pandas: A powerful data manipulation library that provides high-performance data structures and tools for working with structured data, including time series.[https://www.devopsschool.com/blog/top-10-time-series-analysis-tools/]
   • NumPy: A fundamental package for numerical computation in Python.
   • statsmodels: A Python package that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration.
   • Darts: A library making it very easy to produce forecasts using a wide range of models, from ARIMA to deep learning.[https://github.com/MaxBenChrist/awesome_time_series_in_python] Also does ensembling, model selection and more.[https://github.com/MaxBenChrist/awesome_time_series_in_python]
   • Tsfresh: A Python package that automatically calculates and extracts several time series features for classification and regression tasks.[https://paperswithcode.com/task/time-series][https://www.analyticsvidhya.com/blog/2022/05/5-python-libraries-for-time-series-analysis/]

2. R: A powerful statistical programming language with packages like forecast, series, and zoo for time series analysis.[https://www.devopsschool.com/blog/top-10-time-series-analysis-tools/][https://paperswithcode.com/task/time-series]

3. MATLAB: A numerical computing environment that offers built-in functions and toolboxes for time series analysis.[https://www.devopsschool.com/blog/top-10-time-series-analysis-tools/][https://paperswithcode.com/task/time-series]

4. SAS: A software suite widely used for statistical analysis, including time series modeling and forecasting.[https://www.devopsschool.com/blog/top-10-time-series-analysis-tools/][https://paperswithcode.com/task/time-series]

5. Stata: A statistical software package that provides time series analysis capabilities.[https://paperswithcode.com/task/time-series][https://www.devopsschool.com/blog/top-10-time-series-analysis-tools/]

6. Julia: A high-level programming language that offers packages like TimeSeries.jl and Econometrics.jl for time series analysis.[https://www.devopsschool.com/blog/top-10-time-series-analysis-tools/]

7. EViews: A specialized software package designed for time series analysis and econometrics.[https://www.devopsschool.com/blog/top-10-time-series-analysis-tools/]

8. Tableau: A data visualization tool with features for analyzing and visualizing time series data.[https://www.devopsschool.com/blog/top-10-time-series-analysis-tools/][https://paperswithcode.com/task/time-series]

9. Excel: A widely used spreadsheet software with built-in functions for basic time series analysis.[https://www.devopsschool.com/blog/top-10-time-series-analysis-tools/][https://paperswithcode.com/task/time-series]

10. SPSS: A statistical software package that includes time series analysis functions.[https://paperswithcode.com/task/time-series][https://www.devopsschool.com/blog/top-10-time-series-analysis-tools/]

Here are the top 10 time series libraries for Python, based on their popularity, functionality, and relevance to time series analysis:

1. Pandas

   • Use case: Data preprocessing, manipulation, and basic time series operations[https://neptune.ai/blog/time-series-tools-packages-libraries][https://blog.gopenai.com/comparing-the-best-python-libraries-for-time-series-analysis-1d9561d5d6df].
   • Features: Supports resampling, rolling statistics, and time-based indexing. Essential for data cleaning and preparation before applying advanced models[https://blog.gopenai.com/comparing-the-best-python-libraries-for-time-series-analysis-1d9561d5d6df].
   • Example: import pandas as pd, data = pd.read_csv('timeseries.csv', parse_dates=['date'], index_col='date')[https://blog.gopenai.com/comparing-the-best-python-libraries-for-time-series-analysis-1d9561d5d6df].

2. NumPy

   • Use case: Handling multidimensional arrays and matrices, compact representation of dates in time series.
   • Features: High-performance multidimensional array object, datetime64 data type for efficient date handling[https://neptune.ai/blog/time-series-tools-packages-libraries][https://blog.gopenai.com/comparing-the-best-python-libraries-for-time-series-analysis-1d9561d5d6df].
   • Example: Using NumPy's datetime64 data type for date operations[https://neptune.ai/blog/time-series-tools-packages-libraries].

3. Statsmodels

   • Use case: Time series decomposition, autoregressive models, hypothesis testing.
   • Features: Includes tools for statistical modeling and analysis of time series data[https://blog.gopenai.com/comparing-the-best-python-libraries-for-time-series-analysis-1d9561d5d6df][https://mydata.ch/10-time-series-python-libraries-in-2022-for-fast-models/].
   • Example: Using Statsmodels for time series decomposition and autoregressive models[https://blog.gopenai.com/comparing-the-best-python-libraries-for-time-series-analysis-1d9561d5d6df].

4. Statsforecast

   • Use case: Univariate time series forecasting.
   • Features: Includes algorithms like ADIDA, HistoricAverage, CrostonClassic, CrostonSBA, CrostonOptimized[https://mydata.ch/10-time-series-python-libraries-in-2022-for-fast-models/].
   • Example: Using Statsforecast for simple univariate forecasting tasks[https://mydata.ch/10-time-series-python-libraries-in-2022-for-fast-models/].

5. Timescale

   • Use case: Handling large-scale time series data.
   • Features: Provides tools for working with time-series data in Python, including hypertable and hyperloglog[https://www.timescale.com/blog/tools-for-working-with-time-series-analysis-in-python].
   • Example: Using Timescale for efficient storage and querying of large time series datasets[https://www.timescale.com/blog/tools-for-working-with-time-series-analysis-in-python].

6. PyFlux

   • Use case: Bayesian dynamic linear models.
   • Features: Optimized for fast model fitting and inference[https://github.com/lmmentel/awesome-time-series].
   • Example: Using PyFlux for Bayesian dynamic linear modeling tasks[https://github.com/lmmentel/awesome-time-series].

7. Stumpy

   • Use case: Time series data mining.
   • Features: Powerful and scalable for various time series operations[https://github.com/lmmentel/awesome-time-series].
   • Example: Using Stumpy for data mining tasks involving time series data[https://github.com/lmmentel/awesome-time-series].

8. TICC (Temporal Clustering of Clusters)

   • Use case: Segmenting and clustering multivariate time series.
   • Features: Solver for efficiently segmenting and clustering time series data[https://github.com/lmmentel/awesome-time-series].
   • Example: Using TICC for clustering multivariate time series data[https://github.com/lmmentel/awesome-time-series].

9. tick (Time-Dependent Modeling)

   • Use case: Statistical learning with a focus on time-dependent modeling.
   • Features: Module emphasizing time-dependent statistical learning tasks[https://github.com/lmmentel/awesome-time-series].
   • Example: Using tick for time-dependent statistical modeling tasks[https://github.com/lmmentel/awesome-time-series].

10. TimesFM (Time Series Foundation Model) & Time Series Generator

    • Use case: Time series forecasting.
    • Features: TimesFM is a pretrained time-series foundation model, while Time Series Generator addresses the limitation of direct multi-step outputs in Keras[https://github.com/lmmentel/awesome-time-series].
    • Example: Using TimesFM for pre-trained time series forecasting and Time Series Generator for direct multi-step forecasting limitations[https://github.com/lmmentel/awesome-time-series].

Each of these libraries offers unique capabilities and is widely used in time series analysis, depending on the specific requirements of your project.

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

1. Pandas (pandas.pydata.org) - A popular open-source Python library for data manipulation and analysis, including robust time series functionality.

2. TensorFlow (www.tensorflow.org) - An open-source machine learning framework that includes a time series analysis library called TensorFlow Extended (TFX).

3. Prophet (facebook.github.io/prophet/) - An open-source forecasting library developed by Facebook's Core Data Science team, used for time series analysis.

4. Statsmodels (www.statsmodels.org) - A Python module that provides classes and functions for the estimation of many different statistical models, including time series analysis.

5. Darts (unit8co.github.io/darts/) - A Python library for easy manipulation and forecasting of time series data.

6. Pyramid (www.alkaline-ml.com/pmdarima/) - A Python package that provides functions for ARIMA modeling of time series data.

7. Sktime (www.sktime.org) - A unified framework for machine learning with time series, including tools for forecasting, classification, and clustering.

8. Fbprophet (facebook.github.io/prophet/) - Another time series forecasting library developed by Facebook.

9. Tslearn (tslearn.readthedocs.io) - A machine learning toolkit dedicated to time series data, including tools for clustering, classifying and predicting time series.

10. Tsfresh (tsfresh.readthedocs.io) - A Python package that automatically calculates a large number of time series characteristics.

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

1. Pandas - A popular Python library for data manipulation and analysis, including time series data. [1]
   • GitHub: https://github.com/pandas-dev/pandas
   • Documentation: https://pandas.pydata.org/docs/
2. Statsmodels - A Python library for statistical modeling and analysis, including time series analysis. [2]
   • GitHub: https://github.com/statsmodels/statsmodels
   • Documentation: https://www.statsmodels.org/stable/
3. TensorFlow - An open-source machine learning library that includes tools for time series analysis. [3]
   • GitHub: https://github.com/tensorflow/tensorflow
   • Documentation: https://www.tensorflow.org
4. PyTorch - Another popular open-source machine learning library that includes tools for time series analysis. [4]
   • GitHub: https://github.com/pytorch/pytorch
   • Documentation: https://pytorch.org/docs/
5. Kats - A relatively new open-source library for time series analysis, developed by Facebook. [5]
   • GitHub: https://github.com/facebook/kats
   • Documentation: https://facebook.github.io/kats/
6. Prophet - A open-source software for forecasting time series data, developed by Facebook. [6]
   • GitHub: https://github.com/facebook/prophet
   • Documentation: https://facebook.github.io/prophet/
7. Dask - A library for parallel computing in Python that includes tools for time series analysis. [7]
   • GitHub: https://github.com/dask/dask
   • Documentation: https://docs.dask.org/en/stable/
8. Apache Spark - A unified analytics engine for large-scale data processing that includes tools for time series analysis. [8]
   • GitHub: https://github.com/apache/spark
   • Documentation: https://spark.apache.org/docs/latest/
9. GluonTS - A Python library for time series analysis and forecasting that is built on top of Apache MXNet. [9]
   • GitHub: https://github.com/gluonts/gluonts
   • Documentation: https://gluonts.mxai.org/
10. StatsForecast - A Python library for fast and efficient time series forecasting. [10]
    • GitHub: https://github.com/jdb78/statsforecast
    • Documentation: https://statsforecast.readthedocs.io/en/latest/

These libraries are widely used in the industry and academia for various tasks such as data preprocessing, feature engineering, modeling, and forecasting.

References:

[1] pandas-dev. (n.d.). pandas. GitHub. https://github.com/pandas-dev/pandas [2] statsmodels. (n.d.). statsmodels. GitHub. https://github.com/statsmodels/statsmodels [3] tensorflow. (n.d.). TensorFlow. GitHub. https://github.com/tensorflow/tensorflow [4] pytorch. (n.d.). PyTorch. GitHub. https://github.com/pytorch/pytorch [5] facebook. (n.d.). Kats. GitHub. https://github.com/facebook/kats [6] facebook. (n.d.). Prophet. GitHub. https://github.com/facebook/prophet [7] dask. (n.d.). Dask. GitHub. https://github.com/dask/dask [8] apache. (n.d.). Apache Spark. GitHub. https://github.com/apache/spark [9] gluonts. (n.d.). GluonTS. GitHub. https://github.com/gluonts/gluonts [10] jdb78. (n.d.). StatsForecast. GitHub. https://github.com/jdb78/statsforecast

Here are the top 10 time series libraries, widely recognized for their functionality and popularity in data analysis, forecasting, and machine learning:

1. Pandas

   • A powerful Python library for data manipulation and analysis, including time series data. It provides tools for resampling, shifting, and windowing time series data.
   • Source: Pandas Documentation

2. Statsmodels

   • A Python library for statistical modeling, including ARIMA, SARIMA, and other time series forecasting methods.
   • Source: Statsmodels Documentation

3. Prophet

   • Developed by Facebook, Prophet is a forecasting tool for time series data that handles seasonality, holidays, and trends effectively.
   • Source: Prophet Documentation

4. TensorFlow Probability (TFP)

   • A Python library built on TensorFlow that supports probabilistic modeling and Bayesian time series forecasting.
   • Source: TensorFlow Probability Documentation

5. PyTorch Forecasting

   • A PyTorch-based library designed for time series forecasting, offering pre-built models and tools for deep learning applications.
   • Source: PyTorch Forecasting Documentation

6. Sktime

   • A Python library specifically designed for time series analysis, including forecasting, classification, and regression tasks.
   • Source: Sktime Documentation

7. Darts

   • A Python library for time series forecasting, supporting both traditional models and deep learning approaches like RNNs and LSTMs.
   • Source: Darts Documentation

8. TSFresh

   • A Python library for extracting features from time series data, useful for machine learning tasks.
   • Source: TSFresh Documentation

9. Kats

   • A Python library by Facebook for time series analysis, offering tools for detection, forecasting, and feature extraction.
   • Source: Kats Documentation

10. PyCaret

• An open-source Python library for machine learning that includes time series forecasting capabilities as part of its automated ML toolkit.
• Source: PyCaret Documentation

These libraries are widely used in data science, machine learning, and business analytics for handling time series data effectively.

Here are some highly regarded time series libraries for forecasting sales data, along with additional context about each:

1. Prophet:

   • Description: Developed by Facebook, Prophet is designed for forecasting time series data that exhibit seasonal effects and holidays. It is user-friendly and can handle missing data and outliers effectively.
   • Language: Python and R
   • Use Case: Ideal for business forecasting where seasonality and holiday effects are important.

2. Statsmodels:

   • Description: A comprehensive library for statistical modeling in Python that includes extensive capabilities for time series analysis. It offers ARIMA, SARIMA, and other statistical models.
   • Language: Python
   • Use Case: Suitable for users who prefer a statistical approach to time series forecasting and need deep insights into the data.

3. TensorFlow/Keras:

   • Description: While primarily a deep learning framework, TensorFlow can be used for time series forecasting through LSTM (Long Short-Term Memory) networks or other neural architectures.
   • Language: Python
   • Use Case: Best for complex forecasting tasks where capturing non-linear relationships in data is essential.

4. XGBoost:

   • Description: An optimized gradient boosting library that can be used for regression tasks, including time series forecasting. It is known for its performance and speed.
   • Language: Python, R, and others
   • Use Case: Useful for structured data forecasting tasks, especially when combined with feature engineering.

5. pmdarima:

   • Description: An extension of the Statsmodels library that provides a convenient interface for auto ARIMA modeling, which automates the process of finding the best ARIMA parameters.
   • Language: Python
   • Use Case: Great for users who want to automate the ARIMA modeling process without deep statistical knowledge.

6. Tidyverse (forecast package):

   • Description: A collection of R packages designed for data science, the forecast package within Tidyverse provides methods for time series analysis and forecasting, including ETS and ARIMA models.
   • Language: R
   • Use Case: Excellent for users familiar with the R ecosystem who need robust forecasting capabilities.

7. Darts:

   • Description: A Python library specifically designed for easy manipulation and forecasting of time series. It supports various forecasting models, including classical and machine learning approaches.
   • Language: Python
   • Use Case: Good for users looking for a flexible library that integrates multiple forecasting models with simple APIs.

These libraries vary in complexity and functionality, so the best choice will depend on your specific needs, expertise level, and the nature of your sales data.

Here are some Python libraries that are well-regarded for time series forecasting, particularly for sales data:

• Prophet: Developed by Facebook, Prophet is designed for time series data with strong seasonality and historical data spanning multiple seasons.[https://neptune.ai/blog/time-series-tools-packages-libraries][https://learn.microsoft.com/en-us/fabric/data-science/time-series-forecasting] It's robust to missing data and outliers and excels at capturing trend shifts and holiday effects.[https://neptune.ai/blog/time-series-tools-packages-libraries]
• Statsmodels: This library provides a wide range of statistical modeling tools, including comprehensive functions for time series analysis.[https://www.quora.com/What-are-some-Python-libraries-for-working-with-time-series-data-and-forecasting]
• Darts: Darts is a user-friendly library that supports both classical models like ARIMA and Exponential Smoothing (ES) as well as deep learning models.
• scikit-learn: While primarily a general machine learning library, scikit-learn can be used for time series forecasting with models like linear regression.
• AutoTS: This package automates time series forecasting by finding the best model for your data, including preprocessing and ensembling.[https://neptune.ai/blog/time-series-tools-packages-libraries] It includes models like ARIMA, ETS, and VECM.[https://neptune.ai/blog/time-series-tools-packages-libraries]
• PyCaret: This is an AutoML library that allows you to build and test multiple machine learning models with minimal code, including time series models.[https://neptune.ai/blog/time-series-tools-packages-libraries]
• sktime: This library is specifically for time series machine learning, offering algorithms and tools for regression, forecasting, and classification.[https://neptune.ai/blog/time-series-tools-packages-libraries]

For time series forecasting, particularly for sales data, several libraries are highly recommended due to their comprehensive features and ease of use. Here are some top choices:

1. Facebook Prophet

• Pros: Automatically detects trends, seasonality, and holiday effects. Handles missing data points effectively.
• Use Case: Ideal for univariate time series datasets with trends and seasonal structure[https://preset.io/blog/time-series-forecasting-a-complete-guide/][https://www.machinelearningmastery.com/time-series-forecasting-with-prophet-in-python/].

2. Tsfresh

• Pros: Automatically calculates a large number of time series characteristics, such as Fourier transform components and wavelet transform.
• Use Case: Useful for extracting advanced features from time series data, which can then be used in regression or classification tasks[https://neptune.ai/blog/time-series-tools-packages-libraries].

3. Statsmodels

• Pros: Provides methods for time series decomposition, making it easy to analyze trend, seasonal components, and residual or noise.
• Use Case: Convenient for decomposing any time series and analyzing its components[https://neptune.ai/blog/time-series-tools-packages-libraries][https://preset.io/blog/time-series-forecasting-a-complete-guide/].

4. Scikit-learn

• Pros: A general machine learning library that includes tools for time series forecasting.
• Use Case: Useful for applying various machine learning techniques to time series data, such as XGBoost models[https://preset.io/blog/time-series-forecasting-a-complete-guide/].

5. pmdarima

• Pros: Integrates well with pandas, making it ideal for handling and modeling time series data.
• Use Case: Supports automatic ARIMA implementation and is suitable for production environments[https://preset.io/blog/time-series-forecasting-a-complete-guide/].

These libraries offer a range of functionalities that can be tailored to the specific needs of your sales data forecasting project.

For a straightforward and intuitive approach, Facebook Prophet is often recommended due to its ease of use and automatic model parameter tuning[https://preset.io/blog/time-series-forecasting-a-complete-guide/][https://www.machinelearningmastery.com/time-series-forecasting-with-prophet-in-python/]. However, for more advanced feature extraction and analysis, Tsfresh and Statsmodels can be valuable additions to your toolkit[https://neptune.ai/blog/time-series-tools-packages-libraries].

Based on my web search, here are some recommended time series libraries for forecasting sales data:

1. Prophet (by Facebook's Core Data Science Team):

   • A forecasting library that is designed to be easy to use and flexible.
   • Handles trends, seasonality, and holiday effects well.
   • Widely used for forecasting sales, traffic, and other time series data.
   • Open-source and available in Python and R.
   • Source: https://facebook.github.io/prophet/

2. Statsmodels:

   • A Python module that provides classes and functions for the estimation of many different statistical models.
   • Includes a variety of time series analysis functions, including ARIMA, VAR, and Kalman filtering.
   • Useful for more advanced time series modeling and forecasting.
   • Open-source and well-documented.
   • Source: https://www.statsmodels.org/

3. Keras Time Series Forecasting:

   • Part of the Keras deep learning library in Python.
   • Allows for the creation of complex neural network models for time series forecasting.
   • Can handle nonlinear patterns and capture complex relationships in sales data.
   • Requires more setup and expertise in deep learning compared to other libraries.
   • Source: https://keras.io/guides/time_series/

4. ARIMA (Auto-Regressive Integrated Moving Average):

   • A classic and widely-used time series forecasting model.
   • Handles trends, seasonality, and autocorrelation well.
   • Implemented in many libraries like Statsmodels, R's forecast package, and Python's pmdarima.
   • Requires more manual tuning of parameters compared to some newer libraries.
   • Source: https://otexts.com/fpp2/arima.html

The choice of library will depend on the complexity of your sales data, your familiarity with different modeling approaches, and the level of customization you require. I'd recommend starting with Prophet or Statsmodels, which provide a good balance of ease-of-use and flexibility for most sales forecasting use cases.

Recommended Time Series Libraries for Forecasting Sales Data:

Here are some popular and widely-used time series libraries for forecasting sales data:

Python Libraries

1. Statsmodels: A Python library that provides statistical techniques, including time series analysis and forecasting. It includes tools for ARIMA, SARIMA, ETS, and VAR models. [1]
2. Prophet: 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. [2]
3. Pykalman: A Python library for time series analysis using state-space models. It provides tools for Kalman filtering, smoothing, and forecasting. [3]
4. TensorFlow Probability (TFP): A Python library for probabilistic programming and time series analysis. It includes tools for Bayesian modeling and forecasting. [4]

R Libraries

1. forecast: A popular R package for time series forecasting. It provides tools for ARIMA, ETS, and SARIMA models, as well as other techniques like exponential smoothing. [5]
2. caret: An R package for building and testing regression models, including time series forecasting models. [6]
3. tsfresh: An R package for feature extraction from time series data. It provides tools for extracting relevant features for forecasting. [7]

Other Libraries

1. Facebook Prophet (Python): A open-source library for forecasting time series data, developed by Facebook. It's designed for large-scale forecasting tasks. [8]
2. GluonTS (Python): A Python library for time series forecasting, developed by AWS. It provides tools for building and deploying forecasting models. [9]

When choosing a library, consider the following factors:

• Ease of use: How easy is the library to learn and use?
• Model selection: What types of models does the library support?
• Scalability: Can the library handle large datasets and high-performance computing?
• Interpretability: How easy is it to interpret the results and understand the forecasting models?

Example Use Case:

Using Prophet to forecast sales data:

from prophet import Prophet
import pandas as pd

# Load sales data
data = pd.read_csv('sales_data.csv')

# Create a Prophet model
model = Prophet()

# Fit the model to the data
model.fit(data)

# Make a forecast
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)

# Plot the forecast
model.plot(forecast)

References:

[1] Statsmodels. (n.d.). Statsmodels. Retrieved from https://www.statsmodels.org/

[2] Facebook. (n.d.). Prophet. Retrieved from https://facebook.github.io/prophet/

[3] Pykalman. (n.d.). Pykalman. Retrieved from https://pykalman.github.io/

[4] TensorFlow. (n.d.). TensorFlow Probability. Retrieved from https://www.tensorflow.org/probability

[5] Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(1), 1-23.

[6] Kuhn, M. (2019). Caret: R package version 6.0-84.

[7] Fomin, P., & Bania, J. (2019). tsfresh: R package version 1.12.0.

[8] Facebook. (n.d.). Facebook Prophet. Retrieved from https://facebook.github.io/prophet/

[9] AWS. (n.d.). GluonTS. Retrieved from https://gluon-ts.mxcommunity.io/

Certainly! Here are some popular time series libraries that are widely used for forecasting sales data, along with context about each:

1. Prophet

   • Developed by: Facebook (Meta)
   • Description: Prophet is an open-source forecasting tool designed for business applications. It is particularly user-friendly and works well with time series data that has strong seasonality and holidays. It’s great for sales forecasting because it handles missing data and outliers gracefully.
   • Website: https://facebook.github.io/prophet/

2. ARIMA (via statsmodels)

   • Developed by: Statsmodels (Python library)
   • Description: ARIMA (AutoRegressive Integrated Moving Average) is a classic statistical method for time series forecasting. It’s highly customizable and works well for sales data with trends and seasonality. The statsmodels library in Python provides a robust implementation.
   • Website: https://www.statsmodels.org/stable/tsa.html

3. TensorFlow/Keras (Deep Learning)

   • Developed by: Google
   • Description: TensorFlow and Keras are powerful tools for building deep learning models, including recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks, which are effective for complex sales forecasting tasks. These are ideal for large datasets with non-linear patterns.
   • Website: https://www.tensorflow.org/

4. PyCaret (Time Series Module)

   • Developed by: PyCaret
   • Description: PyCaret is a low-code machine learning library that includes a time series module for forecasting. It simplifies the process of building and comparing multiple models, making it a good choice for quick sales forecasting experiments.
   • Website: https://pycaret.org/

5. Sktime

   • Developed by: Sktime community
   • Description: Sktime is a Python library specifically designed for time series analysis and forecasting. It integrates with scikit-learn and provides tools for tasks like forecasting, classification, and regression on time series data. It’s a versatile choice for sales forecasting.
   • Website: https://www.sktime.org/

6. XGBoost (with Time Series Features)

   • Developed by: XGBoost community
   • Description: XGBoost is a gradient boosting framework that can be adapted for time series forecasting by engineering features like lags and rolling statistics. It’s highly effective for sales data with complex patterns.
   • Website: https://xgboost.ai/

7. Darts

   • Developed by: Unit8
   • Description: Darts is a Python library for time series forecasting that supports a wide range of models, including statistical, machine learning, and deep learning approaches. It’s designed to be easy to use and flexible for sales forecasting tasks.
   • Website: https://unit8co.github.io/darts/

Each of these libraries has its strengths, so the best choice depends on your specific needs, such as dataset size, complexity, and the level of customization required.