Exponential smoothing models are a set of Time Series Forecasting techniques that apply weighted averages of past observations, with the weights decaying exponentially over time. These methods are useful for capturing different components of time series data, such as level, trend, and seasonality.
However, their effectiveness depends on the nature of the data. For Datasets with simple patterns, these models can be quite effective, but for more complex series, alternative methods may be necessary.
Methods Implemented
Advanced Alternatives: For complex datasets like stock prices, advanced models such as Forecasting_AutoArima.py may be more appropriate to capture the intricacies of the data.