Top Python libraries for Time Series Analysis in 2022

Moez Ali
6 min readSep 24, 2022

Python libraries and frameworks data scientists must know for time series analysis in 2022

Photo by Jon Tyson on Unsplash

Introduction

A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Time series analysis is the process of using statistical techniques to model and analyze time series data in order to extract meaningful information from them and make forecasts.

Time series analysis is a powerful tool that can be used to extract valuable information from data and make predictions about future events. It can be used to identify trends, seasonal patterns, and other relationships between variables. Time series analysis can also be used to forecast future events, such as sales, demand, or price movements.

If you are working with time series data in Python, there are a number of different libraries that you can use. In this article, we will review most popular libraries in Python in no particular order.

sktime

Sktime is a Python toolkit for working with time-series data. It provides a set of tools for dealing with time-series data, including tools for processing, visualizing, and analyzing data. Sktime is designed to be easy to use and to be extendable, so that new time-series algorithms can be easily implemented.

Sktime provides an extension to scikit-learn API. It includes all of the necessary methods and tools for the efficient resolution of problems involving time-series regression, prediction, and classification. The library contains specialized machine learning algorithms as well as conversion methods for time series. These are features that are not available in any other libraries.

As per the documentation of sktime, “Our aim is to make the time series analysis ecosystem more interoperable and usable as a whole. sktime provides a unified interface for distinct but related time series learning tasks. It features dedicated time series algorithms and tools for composite model building including pipelining, ensembling, tuning and reduction that enables users to apply an algorithm for one task to another.

sktime also provides interfaces to related libraries, for example scikit-learn

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