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    Time Series Forecast

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    I. Convert the Summer Historical Inventory Data into an index

    II. Use the time series data from the converted index to forecast the inventory data for the next year

    III. Graph of choice

    Time Series. A Time series is a sequence of measurements, typically taken at successive points in time. Time series analysis includes a broad spectrum of exploratory and hypothesis testing methods that have two main goals: (a) identifying the nature of the phenomenon represented by the sequence of observations, and (b) forecasting (predicting future values of the time series variable). Both of these goals require that the pattern of observed time series data is identified and more or less formally described. Once the pattern is established, we can interpret and integrate it with other data (i.e., use it in our theory of the investigated phenomenon, e.g., seasonal commodity prices). Regardless of the depth of our understanding and the validity of our interpretation (theory) of the phenomenon, we can extrapolate the identified pattern to predict future events.

    Summer Historical Inventory Data

    Typical Seasonal Demand for Summer Highs

    Actual Demands (in units)

    Month Year 1 Year 2 Year 3 Year 4 Forecast
    1 18,000 45,100 59,800 35,500
    2 19,800 46,530 30,740 51,250
    3 15,700 22,100 47,800 34,400
    4 53,600 41,350 73,890 68,000
    5 83,200 46,000 60,200 68,100
    6 72,900 41,800 55,200 61,100
    7 55,200 39,800 32,180 62,300
    8 57,350 64,100 38,600 66,500
    9 15,400 47,600 25,020 31,400
    10 27,700 43,050 51,300 36,500
    11 21,400 39,300 31,790 16,800
    12 17,100 10,300 31,100 18,900
    Avg.

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    https://brainmass.com/statistics/regression-analysis/321620

    Solution Summary

    This solution is comprised of a detailed analysis of the given problem and step-by-step calculations in EXCEL and provides students with a clear perspective of the underlying concepts of Time Series Forecast Analysis.

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