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 Ltd. 2019 - 433p
1. Introduction to Time Series Analysis and R; Technical requirements; Time series data; Historical background of time series analysis; Time series analysis
2. Working with Date and Time Objects; Technical requirements; The date and time formats; Date and time objects in R
3. The Time Series Object; Technical requirement; The Natural Gas Consumption dataset; The attributes of the ts class
4. Working with zoo and xts ObjectsTechnical requirement; The zoo class
5. Decomposition of Time Series Data; Technical requirement; The moving average function
6. Seasonality analysis technical requirement seasonality types Seasonal analysis with descriptive statistics
7. Correlation analysis technical requirement correlation between two variables Lags analysis
The autocorrelation function the partial autocorrelation function Lag plots
Causality analysis
8. Forecasting strategies technical requirement the forecasting workflow training approaches
9. Forecasting with linear regression technical requirement the linear regression
10. Forecasting with Exponential smoothing models technical requirement ; forecasting with moving average models
11. Forecasting with ARIMA models Technical requirement ;The stationary process
12. Forecasting with machine learning models technical requirement why and when should we use machine learning
9781788629157
R (Computer Program Language) - Time - Series Analysis
005.133 / KRI
Hands On Time Series Analysis with R: Perform Time Series Analysis and Forecasting Using R - Birmingham Packt Publishing Ltd. 2019 - 433p
1. Introduction to Time Series Analysis and R; Technical requirements; Time series data; Historical background of time series analysis; Time series analysis
2. Working with Date and Time Objects; Technical requirements; The date and time formats; Date and time objects in R
3. The Time Series Object; Technical requirement; The Natural Gas Consumption dataset; The attributes of the ts class
4. Working with zoo and xts ObjectsTechnical requirement; The zoo class
5. Decomposition of Time Series Data; Technical requirement; The moving average function
6. Seasonality analysis technical requirement seasonality types Seasonal analysis with descriptive statistics
7. Correlation analysis technical requirement correlation between two variables Lags analysis
The autocorrelation function the partial autocorrelation function Lag plots
Causality analysis
8. Forecasting strategies technical requirement the forecasting workflow training approaches
9. Forecasting with linear regression technical requirement the linear regression
10. Forecasting with Exponential smoothing models technical requirement ; forecasting with moving average models
11. Forecasting with ARIMA models Technical requirement ;The stationary process
12. Forecasting with machine learning models technical requirement why and when should we use machine learning
9781788629157
R (Computer Program Language) - Time - Series Analysis
005.133 / KRI