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
Simple Exponential Smoothing (SES):
- Description: Suitable for forecasting data without trends or seasonality. It applies a constant smoothing factor to past observations.
- Use Case: Best for stationary series where the data fluctuates around a constant mean.
. Double Exponential Smoothing (Holt’s Linear Trend):
- Description: Extends SES by adding a trend component, allowing it to model data with a linear trend.
- Use Case: Ideal for series with a consistent upward or downward trend but no seasonality.
Triple Exponential Smoothing (Holt-Winters):
- Description: Incorporates both trend and seasonal components, making it suitable for data with both linear trends and seasonal patterns.
- Use Case: Effective for series with regular seasonal fluctuations.
Advanced Alternatives: For complex datasets like stock prices, advanced models such as ARIMA may be more appropriate to capture the intricacies of the data.