A Complete Guide to Time Series Analysis & Forecasting in R

A comprehensive time series analysis and forecasting course using R

3.90 (57 reviews)
Udemy
platform
English
language
Data Science
category
instructor
306
students
10.5 hours
content
Jun 2021
last update
$59.99
regular price

What you will learn

Explore and visualize time series data.

Apply and interpret time series regression results.

Understand various methods to forecast time series data.

Use general forecasting tools and models for different forecasting situations.

Utilize statistical program to compute, visualize, and analyze time series data in economics, business, and the social sciences.

Use benchmark methods of time series forecasting.

Use methods for checking whether a forecasting method has adequately utilized the available information.

Forecast using exponential smoothing methods.

Stationarity, ADF, KPSS, differencing, etc.

Forecast using ARIMA, SARIMA, and ARIMAX.

Learn through plenty of rigorous examples and quizzes.

Description

Forecasting involves making predictions. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call center next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments) or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an essential aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.


  • No prior knowledge of R or data science is required.

  • Emphasis on applications of time-series analysis and forecasting rather than theory and mathematical derivations.

  • Plenty of rigorous examples and quizzes for an extensive learning experience.

  • All course contents are self-explanatory.

  • All R codes and data sets and provided for replication and practice.


At the completion of this course, you will be able to

  • Explore and visualize time series data.

  • Apply and interpret time series regression results.

  • Understand various methods to forecast time series data.

  • Use general forecasting tools and models for different forecasting situations.

  • Utilize statistical programs to compute, visualize, and analyze time-series data in economics, business, and the social sciences.

You will learn

  • Exploring and visualizing time series in R.

  • Benchmark methods of time series forecasting.

  • Time series forecasting forecast accuracy.

  • Linear regression models.

  • Exponential smoothing.

  • Stationarity, ADF, KPSS, differencing, etc.

  • ARIMA, SARIMA, and ARIMAX (dynamic regression) models.

  • Other forecasting models.

Content

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

Screenshots

A Complete Guide to Time Series Analysis & Forecasting in R - Screenshot_01A Complete Guide to Time Series Analysis & Forecasting in R - Screenshot_02A Complete Guide to Time Series Analysis & Forecasting in R - Screenshot_03A Complete Guide to Time Series Analysis & Forecasting in R - Screenshot_04

Reviews

Pumeza
August 1, 2022
I am currently working as an electricity load forecasting and I use the methods mentioned here, it is a good learning for me.
Angel
May 6, 2022
I am a begginer in the course topic. The course have explanation of the theory, interpretation and clear examples in R. The content is high quality. When I was watching the videos, the experience was similar to assist a in person class.

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udemy ID
5/31/2021
course created date
7/1/2021
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