Today I learnt about Simple Exponential Smoothing:
- The most basic of the exponentially smoothed methods goes by the name of simple exponential smoothing (SES).
- It is best suited for predictive modeling tasks involving data that exhibits little to no discernible long-term trends or recurring patterns.
- The central concept behind this approach is to presume that future market trends will closely resemble recent historical patterns observed in demand data.
- In other words, the model will primarily rely on historical data to predict future levels of demand without accounting for any potential changes or shifts in consumer behavior or broader economic factors.
- Compared to simpler forecasting methods like naive or moving average models, the exponential smoothing model possesses certain benefits.:
- Exponential smoothing techniques require only three inputs to operate effectively: the latest forecast, the actual value from that time period, and a smoothing constant (or weighting factor) that determines how much importance is assigned to recent data points.
- By using an exponential smoothing method, we can generate forecasts for future time periods based on past performance. These forecasts are deemed accurate because they take into account any discrepancies between predicted and actual outcomes.
- When applying smoothing techniques, we tend to give more emphasis to recent observations compared to earlier ones, making it simpler to identify patterns within the data. This approach allows us to ignore the inherent unpredictability of certain events, resulting in more reliable predictions.