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Hands-On Time Series Analysis with R : Perform Time Series Analysis and Forecasting Using R.
This book introduces you to time series analysis and forecasting with R; this is one of the key fields in statistical programming and includes techniques for analyzing data to extract meaningful insights. You will explore methods, such as prediction with time series analysis, and identify the relati...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2019.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Krispin, Rami. | |
| 245 | 1 | 0 | |a Hands-On Time Series Analysis with R : |b Perform Time Series Analysis and Forecasting Using R. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2019. | ||
| 300 | |a 1 online resource (438 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 500 | |a The decomposition of time series | ||
| 520 | |a This book introduces you to time series analysis and forecasting with R; this is one of the key fields in statistical programming and includes techniques for analyzing data to extract meaningful insights. You will explore methods, such as prediction with time series analysis, and identify the relationship between each data point in the series. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Time-series analysis |x Data processing. | |
| 650 | 0 | |a R (Computer program language) | |
| 650 | 0 | |a Time-series analysis. | |
| 650 | 6 | |a Série chronologique |x Informatique. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 6 | |a Série chronologique. | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 650 | 7 | |a Time-series analysis |2 fast | |
| 650 | 7 | |a Time-series analysis |x Data processing |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Krispin, Rami. |t Hands-On Time Series Analysis with R : Perform Time Series Analysis and Forecasting Using R. |d Birmingham : Packt Publishing, Limited, ©2019 |z 9781788629157 |
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