Forecasting interest in an object over a future period of time using a three-stage time-series analysis process
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United States of America Patent
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Nov 14, 2017
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app pub date -
Aug 10, 2016
filing date -
Aug 31, 2015
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Abstract
Information related to a time series can be predicted. For example, a repetitive characteristic of the time series can be determined by analyzing the time series for a pattern that repeats over a predetermined time period. An adjusted time series can be generated by removing the repetitive characteristic from the time series. An effect of a moving event on the adjusted time series can be determined. The moving event can occur on different dates for two or more consecutive years. A residual time series can be generated by removing the effect of the moving event from the adjusted time series. A base forecast that is independent of the repetitive characteristic and the effect of the moving event can be generated using the residual time series. A predictive forecast can be generated by including the repetitive characteristic and the effect of the moving event into the base forecast.
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