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