Implemented Simple Exponential Smoothing (SES) using the ‘statsmodels’ library for time series forecasting of passenger traffic (‘logan_passengers’ column) in the economic_indicators dataset.
Utilized SES, an exponential smoothing method to predict future passenger counts based on historical data.
Fitted the SES model to the ‘logan_passengers’ data.
The ‘fit’ method estimated the model’s parameters based on the given observations.
After the model was trained the ‘forecast’ function predicted the passenger counts for the subsequent time points.
SES is a basic yet effective method for forecasting time series data by assigning exponentially decreasing weights to past observations, assuming no underlying trend or seasonality.
After performing both SES and ARIMA models the comparision between them is that SES is a basic and straightforward model suitable for simple time series data without trends or seasonality. It’s easy to implement but lacks the ability to capture complex patterns. ARIMA, on the other hand, is a more comprehensive model that can handle various time series patterns by considering trends, seasonality, and irregularities. It offers greater flexibility but requires careful parameter selection and is computationally more intensive.