Modern Time Series Analysis with R : Practical Forecasting and Impact Estimation with Tidy, Reproducible Workflows.

Format:

Book

Author/Creator:

Khandakar, Yeasmin.

Language:

English

Subjects (All):

Time-series analysis.

Forecasting--Statistical methods.

Forecasting.

R (Computer program language).

Physical Description:

1 online resource (630 pages)

Edition:

1st ed.

Place of Publication:

Birmingham : Packt Publishing, Limited, 2026.

Summary:

Gain expertise in modern time series forecasting and causal inference in R to solve real-world business problems with reproducible, high-quality code Key Features Explore forecasting and causal inference with practical R examples Build reproducible, high-quality time series workflows using tidyverse and modern R packages Apply models to.

Contents:

Intro

Modern Time Series Analysis with R

Practical forecasting and impact estimation with tidy, reproducible workflows

Foreword

Contributors

About the authors

About the reviewers

Table of Contents

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Download the color images

Conventions used

Get in touch

Free benefits with your book

How to Unlock

Share your thoughts

Part 1

Setting the Scene: R

1

R, Rstudio, and R packages

1.1 Introduction to the R ecosystem

1.1.1 What is R?

1.1.2 What is RStudio?

1.1.3 What are R packages?

1.2 Downloading R and RStudio

1.3 Installing R and RStudio with admin access

1.3.1 Windows

1.3.2 macOS

1.3.3 Linux

1.3.3.1 Ubuntu

1.3.3.2 Debian

1.4 Installation in a custom location

1.5 Installing R packages

1.5.1 Package installation via the RStudio menu

1.5.2 Package installation via R console

1.5.3 R package pathways

1.5.4 Installing R packages in development

1.6 Loading installed packages

1.7 Updating software and packages

1.7.1 Checking and updating R packages

1.7.2 Checking and updating R and RStudio

1.8 How to choose R packages

1.9 RStudio for productive programming

1.9.1 Do configure global and project level options

1.9.2 Do set up a working directory

1.9.3 Do start with an RStudio project

1.9.4 Do not preserve the workspace between sessions

1.10 The tidyverse package collection

Summary

Further reading

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2

Objects and Functions in R

Technical requirements

2.1 Objects and their names

2.2 Object types

2.3 Data types

2.4 Atomic vectors

2.4.1 Double

2.4.2 Integer

2.4.3 Logical

2.4.4 Character.

2.5 Lists

2.5.1 Matrix

2.5.2 Array

2.6 Coercion

2.7 S3 atomic vectors

2.7.1 Factors

2.7.2 Dates

2.7.3 Date-times

2.7.4 Duration

2.8 S3 lists

2.8.1 Data frames

2.8.2 Tibbles

2.9 Missing values

2.10 Time Series Specific Objects

2.10.1 ts

2.10.2 tsibble

2.11 Functions in R

2.12 Comparisons

2.13 Conditions

2.14 Conditional execution

2.15 Iterations

2.15.1 The apply family

2.15.2 The map family

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3

Data Input/Output in R

3.1 General structure of input/output functions

3.2 Saving and loading files generated by R

3.3 Data files from R packages

3.4 Importing external files

3.4.1 The readr package

3.4.2 The readxl package

3.4.3 The fst package

3.4.4 Import data via RStudio's menu

3.5 Importing data from relational databases

3.5.1 Connecting to databases

3.5.2 Credential security

3.5.2.1 Pre-configuring credentials during ODBC set-up

3.5.2.2 The keyring package

3.5.2.3 The config package

3.5.2.4 The RStudio API

3.6 Using SQL from R

3.6.1 Inline SQL query

3.6.2 SQL query from a file

3.6.3 SQL translations via dplyr

3.6.4 Parameterized query using glue_sql()

3.7 Data governance

Part 2

The Main Character: Time Series

4

Time Series Characteristics

4.1 What is a time series?

4.2 Distinction of time series from other time-indexed data

4.3 How to classify time-indexed data to apply suitable analytics

4.4 Components of a time series

4.4.1 Trend

4.4.2 Seasonality

4.4.3 Cyclical

4.4.4 Random errors or remainders

4.5 Combining time series components

4.6 Stationarity

4.7 Autocorrelation

4.8 Types of time series

Summary.

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5

Time Series Data Wrangling and Visualization

5.1 Parsing date-time variables

5.2 Setting components of date-time

5.3 When to transform data into time series object?

5.4 Wrangling functions

5.5 Visualization functions

5.6 Time plots

5.7 Seasonal plots

5.7.1 Seasonal subseries plots

5.8 Visualizing relationships among time series

5.8.1 Scatter plots

5.8.2 Lag plots

5.9 Interactive plots

6

Business Applications of Time Series Analysis

6.1 Time series characteristics level problem domains

6.1.1 Trend (trend-cycle) analysis

6.1.2 Seasonality analysis

6.1.3 Outlier/anomaly detection

6.2 Inference and attribution problem domains

6.3 Impact measurement problem domains

6.4 Forecasting problem domains

Part 3

The Makeover: Adjusting the Appearance

7

Time Series Adjustments, Transformations, and Decomposition

7.1 Summary of differences: Adjustments, transformations and decompositions

7.2 Rationales for time series adjustments and transformations

7.3 Time series adjustments

7.3.1 Calendar adjustments

7.3.2 Population adjustments

7.3.3 Base adjustments

7.4 Time series transformations

7.4.1 Logarithmic transformation

7.4.2 Box-Cox transformation

7.5 Decomposition plots

7.6 Decomposition using MAs

7.6.1 MA smoothing of data with trends

7.6.2 MA smoothing of data with seasonality

7.7 Trend and seasonal decomposition with the classical method

7.8 Model based decomposition methods

7.9 STL decomposition

7.9.1 Summary of decomposition techniques

8.

Time Series Features

8.1 Time series features

8.2 Statistical summary-based features

8.3 Autocorrelation based features

8.4 STL-based features

8.5 Tiling window features

8.6 Sliding window features

8.7 Statistical test-based features

8.8 Other useful features

8.9 Clustering using time series features

8.9.1 PCA on time series features

8.9.2 Clustering using time series features, UMAP, and K-means

9

Time Series Smoothing and Filtering

9.1 Comparison of time series preprocessing techniques

9.2 Exponentially weighted moving average method (EWMA)

9.3 Time series filtering

9.3.1 The Nyquist frequency and sampling considerations

9.4 Linear filtering with moving average (MA)

9.5 The Hodrick-Prescott filter

9.6 The Kalman filter

Part 4

The Crystal Ball: Forecasting

10

Basics of Forecasting

10.1 Notations

10.2 Forecasting workflow

10.3 fable for forecasting

10.4 Naïve forecasting method

10.5 Seasonal Naïve method

10.6 Forecasting using average

10.7 Time Series Linear Model (TSLM)

10.7.1 Scenario analysis

10.8 Evaluating point forecast accuracy

10.9 Time series cross-validation

11

Exponential Smoothing

11.1 Forecasting methods versus models

11.2 Exponential smoothing forecasting

11.3 Simple Exponential Smoothing (SES)

11.3.1 Component form of the (N, N) method

11.3.2 SES with additive errors: ETS(A, N, N)

11.3.3 SES with multiplicative errors: ETS(M, N, N)

11.4 Holt's linear trend method.

11.4.1 Component form of Holt's linear trend method

11.4.2 Holt's linear trend model with additive errors: ETS(A, A, N)

11.4.3 Holt's linear trend model with multiplicative errors: ETS(M, A, N)

11.4.4 Additive damped trend method

11.5 Holt-Winters' trend and seasonality method

11.5.1 Component form of additive Holt-Winters' method (A, A)

11.5.2 Component form of multiplicative Holt-Winters' method (A, M)

11.5.3 Component form of Holt-Winters' damped method (Ad, A)

11.6 Automated model selection using ETS()

12

ARIMA Forecasting Models

12.1 Basics of ARIMA models

12.2 Autoregressive model (the AR in ARIMA)

12.3 Integration (the I in ARIMA)

12.4 Moving Average model (the MA in ARIMA)

12.5 General structure of an ARIMA model

12.5.1 Non-seasonal ARIMA model

12.5.2 Seasonal ARIMA models

12.6 ARIMA with exogenous variables (ARIMAX)

12.7 Steps in fitting an ARIMA model

12.8 Automatic ARIMA algorithm

12.9 ARIMA and forecast explainability

12.9.1 Impact of the constant term on forecasts

12.9.2 Prediction intervals from ARIMA models

12.9.3 Impact of differencing orders on prediction intervals

12.9.4 Impact of AR order on cyclic forecasts

13

Advanced Computational Methods for Forecasting

13.1 Understanding the connection between forecasting and prediction

13.2 DL forecasting: Neural network

13.2.1 Forecasting using neural networks

13.2.2 Prediction interval for neural networks

13.3 ML forecasting

13.3.1 Application of ML in forecasting

13.3.2 Quantile regularized regression: A brief summary

13.4 Forecasting as a curve fitting: The Prophet method

13.4.1 Prophet forecasting.

13.5 Forecast ensemble.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-80512-430-7

9781805124306

OCLC:

1573146743