Time series models have always been of utmost importance. In simple words, time series analysis allows us to analyze past events and help us make predictions for the future. Organizations, therefore, rely on time series analysis to make better business decisions. With this, they also have the ability to stand ahead in the race. Although there are numerous python libraries for time series analysis, which ones to rely on is an important question to address. In this article, we will talk about the top 10 Python libraries for time series analysis in 2022.

Sktime

This is an open-source python library exclusively designed for time series analysis. It provides an extension to the scikit-learn API for time-series solutions and contains all the required algorithms and tools that are needed for the effective resolution of time-series regression, prediction, and categorization issues.

Darts

Darts is yet another time series Python library that has made its way to the list of the top 10 Python libraries for time series analysis in 2022. Developed by Unit8, Darts is widely known for easy manipulation and forecasting of time series. It can handle large data quite well and supports both univariate and multivariate time series analysis and models.

Pyflux

Yet another open-source python library of time series that deserves a mention is that of Pyflux. The approach followed by this library is majorly useful for problems such as prediction. Here, the users can construct a stochastic pattern in which data and hidden values are processed as random hazards by using joint probability.

Kats (Kits to Analyze Time Series)

This exceptional open-source Python library is developed by researchers at Facebook (now Meta). This time series python library is extremely easy to use and allows one to set up the models quicker without spending much time. Additionally, it can identify patterns, seasonality, and trends.

Prophet

The prophet is one open-source python time-series library that is dedicated to making predictions for one-dimensional time-series datasets. Its capability is such that it can make accurate predictions for data with the trend and seasonal structure by default. Also, it is quite user-friendly.

TSFRESH

TSFRESH stands for "Time Series Feature extraction based on scalable hypothesis tests". This is a complete Python package with various feature extraction methods and a robust feature selection algorithm. A point to note is that TSFRESH is compatible with sklearn, pandas, and numpy.

Flint

How about getting access to an open-source time-series python library that excels at fast parallel time-series operations? This is exactly what Flint has in store for you. This library takes advantage of the natural ordering of time series data in order to provide location-based optimization.

Arrow

Arrow is nothing but a human-friendly approach to creating, manipulating, formatting, and converting dates, times, and timestamps. This python library implements and updates the DateTime type, plugging gaps in functionality as well as providing an intelligent module API.

Orbit

This is yet another Python framework designed for Bayesian time series forecasting and inference. Its framework is built on probabilistic programming packages like PyStan and Uber's own Pyro.

Pastas

This open-source Python time-series library is designed for processing, simulation, and analysis of hydrogeological time series models. It consists of built-in tools for statistically analyzing, visualizing, and optimising time series models.