Open source frameworks for time series

There are few additional open source frameworks that are excellent resources if you want to build and scale your time series solutions:

• Kats is a time series data analysis toolkit that includes a lightweight, easy-to-use, and generalizable framework for performing time series analysis. Time series analysis, as I’ve discussed, is a crucial component of data science and engineering work in industry, from analyzing key statistics and features to detecting regressions and anomalies and anticipating future trends. Kats’ goal is to provide a one-stop shop for time series analysis, covering detection, forecasting, feature extraction/embedding, multivariate analysis, and other services.
• Prophet is a framework for forecasting time series data based on an additive model incorporating yearly, weekly, and daily seasonality, as well as holiday impacts. It works best with time series with substantial seasonal effects and data from multiple seasons. Prophet is resistant to missing data and trend alterations, and it typically handles outliers well.
• PyFlux is a time series analysis and prediction package. Users can select from a variety of modeling and inference choices, and the output can be used for forecasting and retrospection. Users can create a complete probabilistic model in which the data y and latent variables (parameters) z are represented as random variables via a joint probability p(y, z). A probabilistic approach has the advantage of providing a more full view of uncertainty, which is critical for time series applications such as forecasting. Alternatively, within the same unified API, users can just use Maximum Likelihood estimate for speed.
• Sktime is a Python time series analysis library. It offers a uniform interface for a variety of time series learning activities. This comprises time series classification, regression, clustering, annotation, and forecasting at the moment. It includes time series algorithms as well as scikit-learn-compatible tools for building, tuning, and validating time series models.
• Auto_TimeSeries is a sophisticated model-building tool for time series data. It assumes many intelligent defaults since it automates many activities required in a big effort — but you can modify them. Auto_TimeSeries creates predictive models quickly using Statsmodels ARIMA, Seasonal ARIMA, and Scikit-Learn ML. It automatically chooses the best model with the highest specified score. Auto_TimeSeries allows you to create and choose from a variety of time series models, including ARIMA, SARIMAX, VAR, decomposable (trend + seasonality + holidays) models, and ensemble Machine Learning models.
• TimeSynth is a free and open source library that generates synthetic time series for model testing. Regular and irregular time series can be generated by the library. The architecture enables the user to match distinct signals with other structures, resulting in a large array of signals. The various sorts of signals and noise are given below.
• Tsfresh calculates various time series characteristics, or features, automatically. The package also includes methods for assessing the explanatory power and significance of such variables in regression or classification tasks.
• Darts is a Python package that allows for the simple manipulation and forecasting of time series. It includes a wide range of models, from classics like ARIMA to deep neural networks. The models can all be used in the same way, with the same fit() and predict() procedures as scikit-learn. In addition, the library makes it simple to backtest models and aggregate the predictions of several models and external regressors. Darts is capable of handling both univariate and multivariate time series and models. The neural networks can be trained on a variety of time series, and some models provide probabilistic forecasts.
• Orbit is a Python module for forecasting and inference of Bayesian time series. It uses probabilistic programming languages behind the hood and gives a familiar and intuitive initialize-fit-predict interface for time series applications.
• Arrow is a Python module that allows you to create, manipulate, format, and convert dates, times, and timestamps in a logical and user-friendly manner. It implements and improves the datetime type, filling capability gaps and offering an intelligent module API that supports a wide range of frequent creation cases. Simply said, it allows you to work with dates and times while requiring fewer imports and writing far less code.
• Pastas is an open source Python tool that allows you to process, simulate, and analyze hydrological time series (models). The object-oriented framework enables the rapid addition of additional model components. With the built-in optimization, visualization, and statistical analysis tools, time series models may be developed, calibrated, and examined with just a few lines of Python code.

Flow forecast is an open source deep learning framework for time series forecasting. It includes all of the most recent cutting-edge models (transformers, attention models, GRUs) and concepts, as well as interpretability metrics, cloud provider integration, and model serving capabilities. Flow Forecast was the first time series framework to enable transformer-based models, and it is still the only real end-to-end deep learning framework for time series forecasting.