Increasing the value of alpha (α) in an exponential smoothing model would produce which of the following results?

Question

BlackTom AI · Accepted Answer

Exponential smoothing forecasts combine the most recent actual value with the previous forecast using the smoothing constant α:
Forecast_{t+1} = α · Actual_t + (1 − α) · Forecast_t.

1) Relevant knowledge points and concepts
- α (alpha) is the smoothing constant, 0 ≤ α ≤ 1.
- α determines the weight placed on the most recent observation versus the prior forecast.
- Larger α gives more weight to recent actuals (more responsive); smaller α gives more weight to the past forecast (smoother, less responsive).
- Trade-off: higher sensitivity (less lag) comes with higher forecast variability; lower α reduces variability but increases lag.

2) Step-by-step reasoning
- From the formula, the coefficient on Actual_t is α and on Forecast_t is (1 − α).
- Increasing α increases the fraction of the forecast that comes directly from the latest observed demand and reduces the fraction coming from the prior (older) information.
- Therefore the forecast moves closer to recent changes in demand and responds faster to level shifts.

3) Numerical illustration (intermediate steps)
- Suppose Actual_t = 120 and prior Forecast_t = 100.
- If α = 0.10: Forecast_{t+1} = 0.10·120 + 0.90·100 = 12 + 90 = 102.
- If α = 0.80: Forecast_{t+1} = 0.80·120 + 0.20·100 = 96 + 20 = 116.
- The higher α (0.80) produces a forecast much closer to the recent actual (120), showing increased sensitivity.

4) Why the correct answer is correct
- Because α directly controls the weight on the most recent observation, raising α increases responsiveness to recent demand changes. The forecast therefore reacts more quickly to shifts in demand.

5) Why other potential choices would be incorrect
- "Decrease the sensitivity" would be the opposite of what increasing α does.
- "Have no effect" is incorrect because α explicitly appears in the forecasting equation.
- "Reduce variability" is incorrect because increasing α typically increases forecast variability (less smoothing).

Conclusion: Increasing α increases the sensitivity of the forecast to recent demand changes. This matches the provided correct answer.

Increasing the value of alpha (α) in an exponential smoothing model would produce which of the following results?单项选择题

类似问题

The Holt-Winters additive model applies to time series whose amplitude increases or decreases over time.

In exponential smoothing, a large smoothing constant 𝛼 (e.g., close to 1) means:

What is double exponential smoothing designed to capture in a time series?

True or false: In adaptive forecasting, the smoothing coefficients in exponential smoothing models are manually adjusted.

Ft+1 = αdt+ (1 - α)Ft, where, Ft+1= forecast for next period, α = smoothing coefficient, dt = demand from the most recent period, Ft = Forecast for the given period. Identify the equivalent equation to the exponential smoothing model.

In the Blank ______ approach, an exponentially smaller weight is applied to each demand that occurred farther back in time.

A project has a required return of 12.6 percent, an initial cash outflow of $42,100, and cash inflows of $16,500 in Year 1, $11,700 in Year 2, and $10,400 in Year 4. What is the net present value?

更多留学生实用工具

智能学习助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单