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Linear Model Forecasting Software

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The time series model forecasting software predicts the future demand in the coming months.

 The time series model relies on historical data and its principle is to plot the demand data on a graph over time – that why it is called time series. We can easily take as a proxy the actual sales as demand since companies rather keep sales historical data.

So first we take the historical data (more than a year!) and we plot in a chart according to the months. The method is first to find out the main components of the historical demand pattern in order to build the future forecast. In other word, we try first to “understand” the pattern before forecasting its basic components.

In the time series model, the demand pattern can be split into the three main following components:

  1. Seasonal variation (S): A demand variation that occurs due to a result of seasonal impacts. For example, beer sales are higher in the summer, or swimsuit sales are higher in spring and summer than rest of the year.
  2. Trend (T): the demand steadily increases or decreases over time, and its graph is a line.
  3. Random variations (V): random variations are unpredictable but using past data and statistics, it can be estimated for the forecasting.

 

The spreadsheet does all the calculation from the historical data and forecasting inputs such as seasonal pattern.

 

What you need to fill is the following:

1/ Determine your seasonal pattern: Quarterly, 3-Yearly, Bi-Yearly,...) that most corresponds to your business

2/ Paste your historical demand per seasonal pattern

3/ Enjoy the results on the futur buckets !

Please view below the demo video on how to use the spreadsheet.

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