Analyse time series data to make a forecast.  Forecast will be based on:  estimates of the trend for the smoothed data  estimates of seasonal effects.

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Presentation transcript:

Analyse time series data to make a forecast.  Forecast will be based on:  estimates of the trend for the smoothed data  estimates of seasonal effects.  Analysis of cyclic effects is not expected. The criteria for Achievement with Merit are as follows:

Individual seasonal effects (ISE) = Raw data – Moving Mean Note: You can only calculate the ISE’s where you have values for the centred moving mean (CMM) (use the centred moving mean if the order is even) =C7 - E7

The next step is to calculate the average seasonal effect (ASE) The ASE is the average of the ISE for a particular season. For example: To calculate the ASE for September, you need to find the average of the ISE for Sept 01, Sept 02 and Sept 03. You need to do this for each of the seasons. = average(F9,F13,F17)

As we need to use the ASE’s for another calculation, put the values in a column next to the ISE’s as shown below: Note: Copy and Paste VALUES ONLY (Click on Paste Options after pasting to choose Values Only) Notice how these values are the 4 values from below. Be sure to paste values into all rows (fill it top to bottom) as you will need this later.

Prediction = trendline + ASE Prediction for March 2005 = x ASE for March = × = This is the corresponding period for March 2005 The prediction of the avocado sales for March 2005 is $ Note: Predictions require you to show working and a sentence with correct units.

Prediction = trendline + ASE Prediction for December 2005 = x ASE for December = × = This is the corresponding period for December 2005 The prediction of the avocado sales for December 2005 is $82 726

Report on the validity of the analysis. The report will include justified comments on some of the following:  relevance and usefulness of forecast  features of the time series data  appropriateness of the model  improvements to the model  limitations of the analysis  seasonally adjusted data  comparison with related time series data  development and interpretation of an index number series. The criteria for Achievement with Excellence are as follows:

To remove the effect of the seasons on the data, we deseasonalise the data by taking the time series measurement and subtracting the ASE from it. These are seasonally adjusted values (SAV). =C4 – G4

Create a graph showing the time series (raw) data, the centred moving mean and the deseasonalised data.