A Complete Guide to Time Series Analysis & Forecasting in R

Course Curriculum

Chapter 1: Introduction

Lecture 1: Getting started with R

Lecture 2: How to Install packages and import data in Rstudio?

Lecture 3: Getting started with time series forecasting

Lecture 4: What can be forecast?

Lecture 5: Forecasting data and methods

Lecture 6: Types of data

Lecture 7: Time series data examples

Lecture 8: Forecasting patterns (A graphic example)

Lecture 9: Time series forecasting models (Generic forms)

Lecture 10: The basic steps in a forecasting task

Lecture 11: The statistical forecasting perspective

Lecture 12: Some important notations

Chapter 2: Visualizing Time Series (Part 1)

Lecture 1: Introduction to time series plots and ts object in R

Lecture 2: Time plots

Lecture 3: Time series patterns

Lecture 4: Time series patterns examples

Lecture 5: Seasonal and seasonal subseries plots

Lecture 6: Scatterplots to explore the relationship between two variables

Chapter 3: Visualizing Time Series (Part 2)

Lecture 1: Correlation

Lecture 2: Autocorrelation in time series

Lecture 3: Autocorrelation function (ACF) or correlogram

Lecture 4: Time series pattern in ACF plots

Lecture 5: Time series pattern in ACF plots (Examples in R)

Lecture 6: White noise series

Lecture 7: The Ljung-Box test

Lecture 8: Time series graphics summary

Chapter 4: Benchmark Methods (Part 1)

Lecture 1: The forecaster’s toolbox introduction

Lecture 2: Average method of time series forecasting

Lecture 3: Naive method of time series forecasting

Lecture 4: Seasonal naive method of time series forecasting

Lecture 5: Drift method of time series forecasting

Lecture 6: Simple forecasting methods in R (Example 1)

Lecture 7: Simple forecasting methods in R (Example 2)

Lecture 8: Residual diagnostics for time series

Lecture 9: Steps of residual diagnostics (Example in R)

Chapter 5: Benchmark Methods (Part 2)

Lecture 1: Forecast errors

Lecture 2: Splitting time series data into training and test data

Lecture 3: Cross-validation (CV)

Lecture 4: K-fold cross-validation (CV)

Lecture 5: Time series cross-validation

Lecture 6: Testing time series forecast accuracy in R

Lecture 7: Testing time series forecast accuracy using cross-validation (Example in R

Lecture 8: Transformations and adjustments in time series data

Lecture 9: Mathematical adjustments in time series data (Box-Cox transformation)

Lecture 10: Prediction intervals in time series data in R

Lecture 11: The forecaster’s toolbox summary

Chapter 6: Linear Regression (Part 1)

Lecture 1: Time series regression models introduction

Lecture 2: A simple linear regression model

Lecture 3: A graphical representation of a simple linear regression model

Lecture 4: Fitted values and residuals residuals of a simple linear regression model

Lecture 5: OLS to estimate parameter values of a simple linear regression model

Lecture 6: A simple linear regression model in R

Lecture 7: Multiple regression models introduction

Lecture 8: Multiple linear regression model interpretation of coefficient values

Lecture 9: Goodness-of-fit

Lecture 10: Multiple linear regression in R

Lecture 11: Fitted and actual values in the regression model

Lecture 12: Evaluating the regression model

Lecture 13: Evaluating the regression model in R

Chapter 7: Linear Regression (Part 2)

Lecture 1: What to read in a regression output (Example in R)

Lecture 2: Including a trend in time series data

Lecture 3: Dummy variables for time series analysis

Lecture 4: Seasonal dummy variables in time series

Lecture 5: Seasonal dummy variables in time series in R

Lecture 6: Intervention variables using dummy variables

Lecture 7: Intervention variables cases

Lecture 8: Selecting time series regression predictors

Lecture 9: Selecting predictors by adjusted R-squared

Lecture 10: Selecting predictors by Akaike's Information Criterion (AIC)

Lecture 11: Selecting predictors by Corrected Akaike's Information Criterion (AICc)

Lecture 12: Selecting predictors by Schwarz’s Bayesian Information Criterion (BIC) and CV

Lecture 13: Selecting predictors in R

Chapter 8: Linear Regression (Part 3)

Lecture 1: Introduction to sub-set regression for model selection

Lecture 2: Variable selection by forward and backward step-wise regression

Lecture 3: Variable selection by step-wise regression in R

Lecture 4: Forecasting with regression models

Lecture 5: Scenario based forecast in R

Lecture 6: Non-linear regression introduction

Lecture 7: Non-linear regression using log transformations

Lecture 8: Non-linear regressions with linear, exponential, piece-wise, & cubic spline

Lecture 9: Non-linear regressions with linear, exponential, piece-wise, & cubic spline in R

Lecture 10: Homoscedasticity vs. Heteroscedasticity in OLS

Lecture 11: Multicollinearity and variance inflation factor (VIF)

Chapter 9: Time Series Decomposition

Lecture 1: Time series decomposition introduction

Lecture 2: Time series pattern revisited

Lecture 3: Time series components

Lecture 4: Additive model of time series decomposition

Lecture 5: Multiplicative model of time series decomposition

Lecture 6: Seasonal adjustments in time series data

Lecture 7: Moving averages to extract trend-cycle component of a time series

Lecture 8: Moving averages of moving averages