Mastering Time Series Analysis: Forecasting with ARIMA and SARIMA in Python — A Comprehensive Beginner’s Guide

Author(s): Mala Deep

Originally published on Towards AI.

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Data can be categorized into two types based on how and when they are collected: Time Series Data and Cross-Sectional Data. The term time series data refers to data that is collected at regular intervals over time (e.g., daily, monthly, yearly). Cross-Sectional data, on the other hand, is collected from different individuals, groups, or entities at a specific point in time. To put it another way, time series data shows how things change over time, while cross-sectional data shows how things were at a certain point in time across many people or places.

Image created in napkin.ai by Author.

In this blog, you will learn about

What is Forecasting or Time Series Data ForecastingHow to decompose time series data into trend, seasonality, and ResidualWhy do we decompose data in forecastingHow to perform the Augmented Dickey-Fuller Test for Stationarity (ADF) test.What is ARIMA and how to implement ARIMA in PythonWhat is SARIMA and how to implement SARIMA in PythonPerformance Comparison Between ARIMA and SRIMAWhen to choose ARIMA and SARIMA

Let’s start.

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When we take time series data and use them to predict future values based… Read the full blog for free on Medium.

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Published via Towards AI