5. a. Given the following time series, trend, and seasonal index, calculate a (r

Question

5.       a. Given the following time series, trend, and seasonal index, calculate a (recomposed) seasonal forecast for March 2011.       b. The October seasonal index is 122.99. Explain how an index value of 122.99 influences the shape of a graph          of the forecast curve. Series Trend Jan 2007 1.71 3.623 Feb 1.92 3.612 Seasonal Index Mar 3.58 3.601 Jan 55.91 Apr 4.4 3.590 Feb 63.08 May 3.79 3.579 Mar 85.74 Jun 5.52 3.568 Apr 97.11 Jul 5.5 3.556 May 107.58 Aug 5 3.545 Jun 105.69 Sep 5.48 3.534 Jul 144.87 Oct 4.81 3.523 Aug 145.83 Nov 2.42 3.512 Sep 133.32 Dec 1.46 3.501 Oct 122.99 Jan 2008 1.71 3.490 Nov 83.23 Feb 2.46 3.479 Dec 54.64 Mar 2.42 3.468 Apr 1.79 3.457 May 3.63 3.445 Jun 3.54 3.434 Jul 4.88 3.423 Aug 4.96 3.412 Sep 3.63 3.401 Oct 5.46 3.390 Nov 3.08 3.379 Dec 1.75 3.368 Jan 2009 2.13 3.357 Feb 2.58 3.346 Mar 2.75 3.334 Apr 3.15 3.323 May 3.46 3.312 Jun 3.33 3.301 Jul 4.67 3.290 Aug 4.13 3.279 Sep 4.73 3.268 Oct 3.42 3.257 Nov 3.08 3.246 Dec 1.79 3.235 Jan 2010 1.96 3.223 Feb 1.63 3.212 Mar 2.75 3.201 Apr 3.06 3.190 May 4.31 3.179 Jun 3.31 3.168 Jul 3.71 3.157 Aug 5.25 3.146 Sep 3.67 3.135 Oct 3.1 3.124 Nov 2.25 3.112 Dec 2.29 3.101 Jan 2011 3.090 Feb 3.079 Mar 3.068 Apr 3.057 May 3.046 Jun 3.035 Jul 3.024 Aug 3.013 Sep 3.001 Oct 2.990 Nov 2.979 Dec 2.968 5.       a. Given the following time series, trend, and seasonal index, calculate a (recomposed) seasonal forecast for March 2011.       b. The October seasonal index is 122.99. Explain how an index value of 122.99 influences the shape of a graph          of the forecast curve. Series Trend Jan 2007 1.71 3.623 Feb 1.92 3.612 Seasonal Index Mar 3.58 3.601 Jan 55.91 Apr 4.4 3.590 Feb 63.08 May 3.79 3.579 Mar 85.74 Jun 5.52 3.568 Apr 97.11 Jul 5.5 3.556 May 107.58 Aug 5 3.545 Jun 105.69 Sep 5.48 3.534 Jul 144.87 Oct 4.81 3.523 Aug 145.83 Nov 2.42 3.512 Sep 133.32 Dec 1.46 3.501 Oct 122.99 Jan 2008 1.71 3.490 Nov 83.23 Feb 2.46 3.479 Dec 54.64 Mar 2.42 3.468 Apr 1.79 3.457 May 3.63 3.445 Jun 3.54 3.434 Jul 4.88 3.423 Aug 4.96 3.412 Sep 3.63 3.401 Oct 5.46 3.390 Nov 3.08 3.379 Dec 1.75 3.368 Jan 2009 2.13 3.357 Feb 2.58 3.346 Mar 2.75 3.334 Apr 3.15 3.323 May 3.46 3.312 Jun 3.33 3.301 Jul 4.67 3.290 Aug 4.13 3.279 Sep 4.73 3.268 Oct 3.42 3.257 Nov 3.08 3.246 Dec 1.79 3.235 Jan 2010 1.96 3.223 Feb 1.63 3.212 Mar 2.75 3.201 Apr 3.06 3.190 May 4.31 3.179 Jun 3.31 3.168 Jul 3.71 3.157 Aug 5.25 3.146 Sep 3.67 3.135 Oct 3.1 3.124 Nov 2.25 3.112 Dec 2.29 3.101 Jan 2011 3.090 Feb 3.079 Mar 3.068 Apr 3.057 May 3.046 Jun 3.035 Jul 3.024 Aug 3.013 Sep 3.001 Oct 2.990 Nov 2.979 Dec 2.968

Explanation / Answer

let us discuss what is seasonal index,

A forecasting tool used to determine demand for various commodities or goods in a given marketplace over the course of a typical year (or a shorter time period). Such an index is based on data from previous years that highlights seasonal differences in consumption. In some industries, the seasonality index experiences huge swings. For example, toy makers are likely to experience peak demand in the month leading up to Christmas. Data from a seasonality index may be used to determine safety lead times or safety stock levels.

in our problem the seosonal index for oct is 122.99, which 4th highest index in the year. the 1st 3 are 144.87, 145.85, 133.32 for the month jul. aug,sep. so we can comment that the sale is 4th highest in the month october.

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