Final answer:
The trend-adjusted exponential smoothing forecast utilizes the alpha (α) and beta (β) coefficients to update the smoothed forecast and trend. These parameters must be properly selected to reflect actual demand patterns for accurate forecasted values and trends.
Step-by-step explanation:
To calculate the trend-adjusted exponential smoothing forecast with the given parameters of α = 0.4, β = 0.2, a last trend-adjusted forecast (taf5) = 689.00, and a last trend (t5) = 2.00, we need to follow a systematic process. Assuming we have access to the actual demand for period 6, let's denote it as D6. The procedure is:
- Calculate the smoothed forecast for period 6: F6 = α * D6 + (1 - α) * (taf5 + t5).
- Update the trend value for period 6: T6 = β * (F6 - taf5) + (1 - β) * t5.
- Calculate the trend-adjusted forecast for period 7: taf7 = F6 + T6.
The parameter α (α = 0.4) determines the weight given to the most recent actual demand in adjusting the forecast, while β (β = 0.2) determines the weight given to the most recent trend in adjusting the forecast. A higher α or β puts more emphasis on recent observations, making the model more responsive to changes but also potentially more volatile. The forecast accuracy depends on the appropriateness of these parameters and how closely they align with the actual demand patterns.
The historical demand patterns are essential in setting accurate values for α and β because they reflect the underlying trends and seasonality in the data, influencing the forecasted values and trends predicted by the model.