Introduction to Time Series Analysis in Python

Introduction to Time Series Analysis in Python

Data that is updated in real-time requires additional handling and special care to prepare it for machine learning models. The important Python library, Pandas, can be used for most of this work, and this tutorial guides you through this process for analyzing time-series data.

A _**_time series**_ is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average._

So any dataset in which is taken at successive equally spaced points in time. For example, we can see this data set that is Value of Manufacturers’ Shipments for All Manufacturing Industries.

We will see some important points that can help us in analyzing any time-series dataset. These are:

  • Loading time series dataset correctly in Pandas
  • Indexing in Time-Series Data
  • Time-Resampling using Pandas
  • Rolling Time Series
  • Plotting Time-series Data using Pandas

2020 sep tutorials overviews pandas python time time series

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