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Linear Trend Forecast Analysis

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For the time series below
a) Develop a linear trend forecast
b) Add a quadratic component to the model in part (a) and develop a new forecast
c) Now assume a seasonal pattern and add the seasonality of the months to the linear trend model in part (a) and develop a new forecast
d) Provide the MSE from the 3 models
e) Based on your answers to part d, for whichever forecast model you deem to be the best, draw a time series graph of both the original time series and the "best" forecast

Year Month Time Period COGS
1 1 1 21637
2 2 25506
3 3 26594
4 4 21933
5 5 23117
6 6 26979
7 7 28337
8 8 28926
9 9 25015
10 10 28196
11 11 24170
12 12 25004
2 1 13 24575
2 14 35848
3 15 35217
4 16 32759
5 17 36542
6 18 36109
7 19 40279
8 20 37816
9 21 36338
10 22 37814
11 23 39359
12 24 34151
3 1 25 39862
2 26 50664
3 27 48533
4 28 45382
5 29 43981
6 30 41276
7 31 44284
8 32 46920
9 33 47469
10 34 43187

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Solution Summary

The solution provides a step-by-step analysis as how to perform a linear trend forecast where data exhibits seasonality pattern.

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  • MSc, Osmania University
  • MSc, Indian Institute of Technology - Roorkee (I.I.T.-ROORKEE)
  • BSc, Banaras Hindu University
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