A Complete Tutorial on Time Series Modeling in R

Author: Andrea Manero-Bastin

This article was written by Tavish Srivastava.

Overview

Time Series Analysis and Time Series Modeling are powerful forecasting tools
A prior knowledge of the statistical theory behind Time Series is useful before Time series Modeling
ARMA and ARIMA are important models for performing Time Series Analysis

Introduction

‘Time’ is the most important factor which ensures success in a business. It’s difficult to keep up with the pace of time. But, technology has developed some powerful methods using which we can ‘see things’ ahead of time. Don’t worry, I am not talking about Time Machine. Let’s be realistic here!

I’m talking about the methods of prediction & forecasting. One such method, which deals with time based data is Time Series Modeling. As the name suggests, it involves working on time (years, days, hours, minutes) based data, to derive hidden insights to make informed decision making.

Time series models are very useful models when you have serially correlated data. Most of business houses work on time series data to analyze sales number for the next year, website traffic, competition position and much more. However, it is also one of the areas, which many analysts do not understand.

So, if you aren’t sure about complete process of time series modeling, this guide would introduce you to various levels of time series modeling and its related techniques.

Table of Contents

Basics – Time Series Modeling
Exploration of Time Series Data in R
Introduction to ARMA Time Series Modeling
Framework and Application of ARIMA Time Series Modeling

Time to get started!

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