Explanation of time series analysis for demand forecasting

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Reference no: EM132118860

Question - What is not true about the explanation of time series analysis for demand forecasting? (The answer can be one or more). Why did you choose this answer?

1. Time series analysis is based on the assumption that the past pattern of demand, which is a dependent variable, will continue in the future.

2. The naive method is a technique to forecast demand for the next period with the latest demand, and can be used effectively when the demand is stable.

3. The moving average method is a useful technique when only random variation is largely applied. The latest n period data can be arithmetically averaged or weighted averaged to forecast the demand for the next period.

4. Trend analysis can predict future demand by finding a straight trend line that minimizes the sum of the errors between the past demand and trend forecasts, if there is a noticeable increase or decrease in the past data.

5. The exponential smoothing method can analyze trends and seasonal variations, but even if the smoothing constant is reduced, the weight of recent demand data can not be imposed less than the weight of past demand data.

Reference no: EM132118860

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