Introduction
Getting started with R
How to Install packages and import data in Rstudio?
Getting started with time series forecasting
What can be forecast?
Forecasting data and methods
Types of data
Time series data examples
Forecasting patterns (A graphic example)
Time series forecasting models (Generic forms)
The basic steps in a forecasting task
The statistical forecasting perspective
Some important notations
Visualizing Time Series (Part 1)
Introduction to time series plots and ts object in R
Time plots
Time series patterns
Time series patterns examples
Seasonal and seasonal subseries plots
Scatterplots to explore the relationship between two variables
Visualizing Time Series (Part 2)
Correlation
Autocorrelation in time series
Autocorrelation function (ACF) or correlogram
Time series pattern in ACF plots
Time series pattern in ACF plots (Examples in R)
White noise series
The Ljung-Box test
Time series graphics summary
Benchmark Methods (Part 1)
The forecaster’s toolbox introduction
Average method of time series forecasting
Naive method of time series forecasting
Seasonal naive method of time series forecasting
Drift method of time series forecasting
Simple forecasting methods in R (Example 1)
Simple forecasting methods in R (Example 2)
Residual diagnostics for time series
Steps of residual diagnostics (Example in R)
Benchmark Methods (Part 2)
Forecast errors
Splitting time series data into training and test data
Cross-validation (CV)
K-fold cross-validation (CV)
Time series cross-validation
Testing time series forecast accuracy in R
Testing time series forecast accuracy using cross-validation (Example in R
Transformations and adjustments in time series data
Mathematical adjustments in time series data (Box-Cox transformation)
Prediction intervals in time series data in R
The forecaster’s toolbox summary
Linear Regression (Part 1)
Time series regression models introduction
A simple linear regression model
A graphical representation of a simple linear regression model
Fitted values and residuals residuals of a simple linear regression model
OLS to estimate parameter values of a simple linear regression model
A simple linear regression model in R
Multiple regression models introduction
Multiple linear regression model interpretation of coefficient values
Goodness-of-fit
Multiple linear regression in R
Fitted and actual values in the regression model
Evaluating the regression model
Evaluating the regression model in R
Linear Regression (Part 2)
What to read in a regression output (Example in R)
Including a trend in time series data
Dummy variables for time series analysis
Seasonal dummy variables in time series
Seasonal dummy variables in time series in R
Intervention variables using dummy variables
Intervention variables cases
Selecting time series regression predictors
Selecting predictors by adjusted R-squared
Selecting predictors by Akaike's Information Criterion (AIC)
Selecting predictors by Corrected Akaike's Information Criterion (AICc)
Selecting predictors by Schwarz’s Bayesian Information Criterion (BIC) and CV
Selecting predictors in R
Linear Regression (Part 3)
Introduction to sub-set regression for model selection
Variable selection by forward and backward step-wise regression
Variable selection by step-wise regression in R
Forecasting with regression models
Scenario based forecast in R
Non-linear regression introduction
Non-linear regression using log transformations
Non-linear regressions with linear, exponential, piece-wise, & cubic spline
Non-linear regressions with linear, exponential, piece-wise, & cubic spline in R
Homoscedasticity vs. Heteroscedasticity in OLS
Multicollinearity and variance inflation factor (VIF)
Time Series Decomposition
Time series decomposition introduction
Time series pattern revisited
Time series components
Additive model of time series decomposition
Multiplicative model of time series decomposition
Seasonal adjustments in time series data
Moving averages to extract trend-cycle component of a time series
Moving averages of moving averages
Moving averages (Example in R)
Classical decomposition of a time series
Classical decomposition issues and example in R
X11 decomposition of a time series (with example in R)
SEATS decomposition of a time series (with example in R)
STL decomposition of a time series (with example in R)
Time series forecasting using decomposition (with example in R)
Time series decomposition summary
Exponential Smoothing
Exponential smoothing introduction
Simple exponential smoothing
Simple exponential smoothing in component form
Simple exponential smoothing (example in R)
Holt’s linear trend method
Holt’s linear trend method (example in R)
Holt’s damped trend method with example in R
Forecasting performance of SES, Holt's trend, and damped trend methods in R
Holt-Winters’ seasonal method (additive, multiplicative, & damped)
Holt Winters' seasonal method in R
A summary of exponential smoothing methods
Innovations state space models (ETS models)
Estimation and model selection of ETS
ETS models in R
Exponential smoothing models summary
ARIMA Models (Part 1)
ARIMA models introduction
Stationarity of a time series
Time series stationarity examples
Differencing a time series to achieve stationarity
Unit root detection using the ACF and the Ljung-Box test
The augmented Dickey-Fuller (ADF) test for time series stationarity
The KPSS test for time series stationarity in R
Algorithm to test stationarity of a time series
Various time series stationarity tests in R (ACF, the Ljung-Box, ADF, KPSS)
ARIMA Models (Part 2)
Autoregressive models
Moving average models
Non-seasonal ARIMA models
Non-seasonal ARIMA models in R
Building your own ARIMA models
The difference between ACF and PACF
Choosing AR(p) and MA(q) in ARIMA models using ACF and PACF
How to pick the value of p in ARIMA models using ACF & PACF?
How to pick the value of q in ARIMA models using ACF & PACF?
ARIMA modelling procedure
Non-seasonal ARIMA models example in R
ARIMA Models (Part 3)
Introduction to seasonal ARIMA (SARIMA) models
Choosing P & Q in SARIMA models using ACF and PACF
Seasonal ARIMA (SARIMA) models in R
ARIMA models summary
Dynamic regression (ARIMAX) models
Dynamic regression (ARIMAX) models
How to model dynamic regression (ARIMAX) models?
Estimation of dynamic regression (ARIMAX) models
Dynamic regression (ARIMAX) models in R