Cyclic Cash Flows



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    David Esh

    I believe you should first attempt to convert the time series into a stationary series.  Model the stationary series, then convert back into seasonally-affected series.

    This could be done with seasonal indices, followed by first differences on the adjusted data (or other transformations of your series).  The Dickey-Fuller test can be used to test for stationarity (to see if your transforms worked).  I think what you are describing is a form of developing seasonal indices (what you call weighting factors, i.e. multiplicative seasonal adjustments), and that is on the right track. Unless you have a long time series to accurately define the standard deviation/variance/volatility of  November credits for example, the uncertainties in your estimates are likely to be large due to the small sample sizes.  Perhaps you could aggregate data for similar businesses, that would come with its own problems. 

    Whatever approach you use, you can test it by running the results into a submodel and calculating your simulated time series properties and comparing to the actual time series properties.

    Hope that helps.

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