Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R
Krispin, Rami
Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R - Birmingham: Packt Publishing Limited, 2019. - vi, 433p.
Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Time Series Analysis and R; Technical requirements; Time series data; Historical background of time series analysis; Time series analysis; Learning with real-life examples; Getting started with R; Installing R; A brief introduction to R; R operators; Assignment operators; Arithmetic operators; Logical operators; Relational operators; The R package; Installation and maintenance of a package; Loading a package in the R working environment; The key packages VariablesImporting and loading data to R; Flat files; Web API; R datasets; Working and manipulating data; Querying the data; Help and additional resources; Summary;
Chapter 2: Working with Date and Time Objects; Technical requirements; The date and time formats; Date and time objects in R; Creating date and time objects; Importing date and time objects; Reformatting and converting date objects; Handling numeric date objects; Reformatting and conversion of time objects; Time zone setting; Creating a date or time index; Manipulation of date and time with the lubridate package Reformatting date and time objects
the lubridate wayUtility functions for date and time objects; Summary;
Chapter 3: The Time Series Object; Technical requirement; The Natural Gas Consumption dataset; The attributes of the ts class; Multivariate time series objects; Creating a ts object; Creating an mts object; Setting the series frequency; Data manipulation of ts objects; The window function; Aggregating ts objects; Creating lags and leads for ts objects; Visualizing ts and mts objects; The plot.ts function; The dygraphs package; The TSstudio package; Summary
Chapter 4: Working with zoo and xts ObjectsTechnical requirement; The zoo class; The zoo class attributes; The index of the zoo object; Working with date and time objects; Creating a zoo object; Working with multiple time series objects; The xts class; The xts class attributes; The xts functionality; The periodicity function; Manipulating the object index; Subsetting an xts object based on the index properties; Manipulating the zoo and xts objects; Merging time series objects; Rolling windows; Creating lags; Aggregating the zoo and xts objects; Plotting zoo and xts objects The plot.zoo functionThe plot.xts function; xts, zoo, or ts
which one to use?; Summary; Chapter 5: Decomposition of Time Series Data; Technical requirement; The moving average function; The rolling window structure; The average method; The MA attributes; The simple moving average; Two-sided MA; A simple MA versus a two-sided MA; The time series components; The cycle component; The trend component; The seasonal component; The seasonal component versus the cycle component; White noise; The irregular component; The additive versus the multiplicative model; Handling multiplicative series
9781788629157
005.133 / KRI/H
Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R - Birmingham: Packt Publishing Limited, 2019. - vi, 433p.
Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Time Series Analysis and R; Technical requirements; Time series data; Historical background of time series analysis; Time series analysis; Learning with real-life examples; Getting started with R; Installing R; A brief introduction to R; R operators; Assignment operators; Arithmetic operators; Logical operators; Relational operators; The R package; Installation and maintenance of a package; Loading a package in the R working environment; The key packages VariablesImporting and loading data to R; Flat files; Web API; R datasets; Working and manipulating data; Querying the data; Help and additional resources; Summary;
Chapter 2: Working with Date and Time Objects; Technical requirements; The date and time formats; Date and time objects in R; Creating date and time objects; Importing date and time objects; Reformatting and converting date objects; Handling numeric date objects; Reformatting and conversion of time objects; Time zone setting; Creating a date or time index; Manipulation of date and time with the lubridate package Reformatting date and time objects
the lubridate wayUtility functions for date and time objects; Summary;
Chapter 3: The Time Series Object; Technical requirement; The Natural Gas Consumption dataset; The attributes of the ts class; Multivariate time series objects; Creating a ts object; Creating an mts object; Setting the series frequency; Data manipulation of ts objects; The window function; Aggregating ts objects; Creating lags and leads for ts objects; Visualizing ts and mts objects; The plot.ts function; The dygraphs package; The TSstudio package; Summary
Chapter 4: Working with zoo and xts ObjectsTechnical requirement; The zoo class; The zoo class attributes; The index of the zoo object; Working with date and time objects; Creating a zoo object; Working with multiple time series objects; The xts class; The xts class attributes; The xts functionality; The periodicity function; Manipulating the object index; Subsetting an xts object based on the index properties; Manipulating the zoo and xts objects; Merging time series objects; Rolling windows; Creating lags; Aggregating the zoo and xts objects; Plotting zoo and xts objects The plot.zoo functionThe plot.xts function; xts, zoo, or ts
which one to use?; Summary; Chapter 5: Decomposition of Time Series Data; Technical requirement; The moving average function; The rolling window structure; The average method; The MA attributes; The simple moving average; Two-sided MA; A simple MA versus a two-sided MA; The time series components; The cycle component; The trend component; The seasonal component; The seasonal component versus the cycle component; White noise; The irregular component; The additive versus the multiplicative model; Handling multiplicative series
9781788629157
005.133 / KRI/H