Time series data is everywhere—stock prices, weather patterns, website traffic, economic indicators, and even your heartbeat. If you want to forecast the future based on the past, you need time series analysis.
And if you want to learn it hands-on, one book stands out as a practical favorite: by Woodward, Gray, and Elliott. applied time series analysis with r pdf
| Chapter | Topic | R Package You’ll Use | |---------|----------------------------|----------------------| | 1 | Basic descriptive analysis | stats , ggplot2 | | 2 | Stationarity & autocorrelation | forecast , tseries | | 3 | ARMA/ARIMA models | forecast::auto.arima() | | 4 | Seasonal models (SARIMA) | seasonal | | 5 | Spectral analysis & periodicity | spectral | | 6 | GARCH for volatility | rugarch | | 7 | Multivariate time series (VAR) | vars | | Chapter | Topic | R Package You’ll
(to test stationarity):