Time Series Analysis And Forecasting Using R

learn Time series analysis, forecasting and business analytics with the perspective of a data scientist

What you'll learn

Methods of Forecasting and Steps in Forecasting

Problems in Forecasting and Simple Forecasting Methods

Simple and Multiple Regression and Time Series Decomposition

Exponential Smoothing and ARIMA models

Requirements

Basic knowledge in statistics, mathematics, programming

Basic knowledge of using R and Excel

Description

Learn how to effectively work around business analytics to find out answers to key questions related to business. We are using sophisticated statistical tools like R and excel to analyze data. This training is a practical and a quantitative course which will help you learn business analytics with the perspective of a data scientist. The learner of this course will learn the most relevant techniques used in the real world by data analysts of companies around the world.The training includes the following;Introduction to ForecastingModels/Methods of ForecastingSteps in ForecastingProblems in ForecastingSimple Forecasting MethodsSimple and Multiple RegressionTime Series DecompositionExponential SmoothingARIMA modelsConclusionTime series in R is defined as a series of values, each associated with the timestamp also measured over regular intervals (monthly, daily) like weather forecasting and sales analysis. The R stores the time series data in the time-series object and is created using the ts() function as a base distribution. How Time-series works in R?R has a powerful inbuilt package to analyze the time series or forecasting. Here it builds a function to take different elements in the process. At last, we should find a better fit for the data. The input data we use here are integer values. Not all data has time values, but their values could be made as time-series data. The data consists of observations over a regular interval of time. It needs several transformations before it is modeled up.

Overview

Section 1: Introduction

Lecture 1 Introduction to Business Analytics Forecasting

Section 2: Getting Started

Lecture 2 What is Forecasting

Lecture 3 What is Forecasting Continues

Lecture 4 Methods of Forecasting

Lecture 5 Steps of Forecasting

Lecture 6 Problems with Forecasting

Section 3: Simple Forecasting Methods

Lecture 7 Simple Forecasting Methods

Lecture 8 Methods in Simple Forecasting Methods

Lecture 9 Example of Simple Forecasting Methods

Section 4: Transformations and Adjustments

Lecture 10 Transformations and Adjustments

Lecture 11 Transformations and Adjustments Example

Lecture 12 Forecasting Accuracy

Lecture 13 Simple Regression in Forecasting

Lecture 14 Simple Regression in Forecasting Continues

Section 5: Simple Regression and Multiple Linear Regression

Lecture 15 Example of Simple Regression in Forecasting

Lecture 16 Non Linear Regression

Lecture 17 Forecasting with Regression

Lecture 18 Time Series Regression

Lecture 19 Time Series Regression Continues

Lecture 20 Multiple Linear Regression

Lecture 21 Predictors Forecasting for Formula

Section 6: Time Series Decomposition

Lecture 22 Time Series Decomposition

Lecture 23 Time Series Decomposition Continues

Lecture 24 Forecasting with Decomposition

Lecture 25 Exponential Smoothing in Forecasting

Lecture 26 ARIMA Modelling

Section 7: Model

Lecture 27 Auto Regressive Model

Lecture 28 Moving Average Model

Lecture 29 Non Seasonal ARIMA

Lecture 30 ACF and PACF plot in Forecasting

Lecture 31 More on ARIMA Modelling

Lecture 32 Seasonal ARIMA Modelling

Students, Marketing professionals, Market Researchers, Product Managers, Any person running a business