Time Series Forecasting Techniques

To begin, one of the fundamental methods I've employed is the ARIMA (AutoRegressive Integrated Moving Average) model. This model is exceptionally versatile, allowing me to model data with trends and seasonalities by adjusting its parameters. In R, the forecast package provides comprehensive functions such as auto.arima(), which simplifies the process of model selection by automatically determining the optimal parameters. The beauty of ARIMA lies in its flexibility and robustness in handling a wide array of time series data. Another powerful technique is Exponential Smoothing (ETS), which I've found particularly useful for data with seasonal patterns...