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    Describing Time-Series Forecasting

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    In general terms, describe what time-series forecasting models are. Explain, briefly, why the larger number of periods included in a moving average forecast, the less well the forecast identifies rapid changes in the variable of interest.

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    Time-series forecasting models are attempts to predict certain things about the future, based on observations of those things in the past. For example, we might try and predict the rainfall during the month of August 2011 in New York City, based on the amount of rain New York City got in the last ten Augusts. The method by which we make this prediction is the model part, and the number we come up with (e.g. 13 inches of rain) is the forecast.

    One type of time-series forecasting model is a moving average forecast. In this model, we predict the series value at ...

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    Solution describes time-series forecasting and provides examples.

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