000			01953nam a2200193 4500
003			OSt
005			20240529110655.0
008			191128b ||||| |||| 00| 0 eng d
020			_a9781788629157
040			_c
082			_a005.133 _bKRI
100			_aKrispin, Rami _945454
245			_aHands On Time Series Analysis with R: Perform Time Series Analysis and Forecasting Using R
260			_aBirmingham _bPackt Publishing Ltd. _c2019
300			_a433p
500			_a1. 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
600			_aR (Computer Program Language) - Time - Series Analysis _945455
942			_2ddc _cLB _k005.133 _mKRI
999			_c119041 _d119041