To select a value for α for exponential smoothing
Group of answer choices

use a small α when the series varies substantially.

use a large α when the series has little random variability.

All of the alternatives are true.

use a value between 0 and 1

The correct answer is:

**All of the alternatives are true.**

Here's why:
- **Use a small α when the series varies substantially**: A smaller α gives more weight to past observations, which is helpful when the data is volatile, so it smooths out random fluctuations.
- **Use a large α when the series has little random variability**: A larger α gives more weight to recent observations, which is effective when the data has less random variation and trends can be captured more quickly.
- **Use a value between 0 and 1**: This is a fundamental rule of exponential smoothing; the smoothing constant α must always be between 0 and 1 to ensure a meaningful combination of previous and current observations.

Do you need more details on exponential smoothing or have any other questions?

Here are five related questions:
1. How does exponential smoothing differ from moving averages?
2. What happens when you set α to 1 or 0?
3. How do you select an optimal value of α for a given dataset?
4. What are the limitations of exponential smoothing in forecasting?
5. How does exponential smoothing handle seasonal variations?

**Tip:** When using exponential smoothing, test different α values to see which provides the most accurate forecasts based on past performance.

To select a value for α for exponential smoothing

Understand how to select the optimal α value for exponential smoothing in time series analysis. Explore when to use small or large α values based on data volatility, and ensure proper implementation by following the rule that α must be between 0 and 1. Suitable for undergraduate students.

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