I need to automatically identify if a time series is intermittent or not. Depending on the result I'll use one or another method for forecasting it.
Is there any test to detect intermittent time series?
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2 Answers
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Just set a threshold like 30% and if the number of "zeroes" exceeds this threshold then declare it to be an intermittent demand series. For guidelines to deal with "unusual demands" rather than believing them and Level Shifts ( n.b. A level Shift is not a time trend ) . Also since intermittent demand can yield rates that are auto-regressive ( i.e autocorrelated ) models like the Poisson Model or the Croston approach are of limited value. Please see the discussion Please see my comments in How to forecast based on aggregated data over irregular intervals? regarding this.
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1$\begingroup$ Hi Dave. I never heard the term intermittent time series before. Does that mean that it is 0 for a time then behaves like a statioanry time series then go back to 0 for a while and so on. $\endgroup$ Commented May 8, 2012 at 0:26
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$\begingroup$ @MichaelChernick yes .. exactly , It arises when there is sales. Consider a time series of the number of gallons of gas that you put into your car on a daily basis. $\endgroup$ Commented Jun 11, 2012 at 18:41
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If this is for a forecasting purpose, I advice that you do a forecasting segmentation as Intermittent time series can be categories into 4 segments(i.e Intermittent, Lumpy, Smooth, Erratic). Using ADI(Average Demand Interval) AND CV^2(Co-efficient of Variation). After doing this, you can then apply different forecasting methods for each segmentation.
An intermittent time series refers to a set of data points that are observed irregularly or sporadically over time. Unlike a regular time series, where data is collected at a fixed interval, intermittent time series may have gaps or missing values between observations.
For example, if you are tracking the number of customers who visit a store, and you only record the data when someone makes a purchase, you would have an intermittent time series. This is because you are not collecting data at regular intervals but only when there is an event (i.e., a customer makes a purchase).
Intermittent time series can be challenging to analyze and forecast because of the irregularity in the data. However, there are statistical methods and techniques that can be used to analyze intermittent time series and make predictions based on the available data.
