CODEWITHSUNDEEP
rtime-seriesforecasting
shweta
shweta

Reputation: 41

How to deal with time series data with many 0's?

I have time series data ranging from 0 to 30 million. Its basically web traffic weekly data. I am working on building a forecasting model with this data. I want to understand how can I deal with this range of data. I tried box cox transformation with prophet model. I am not sure about what metrics could I use to evaluate the performance of the model. The data has a lot of 0's. I can't remove them from the dataset. Is there a better way to deal with the 0's other than the Box Cox transformation? I had issues with the inverse transformation but I added a small value (0.1) to the data to avoid negative values.

Upvotes: 2

Views: 6963

Answers (1)

Rajith Thennakoon
Rajith Thennakoon

Reputation: 4130

If your series have lot of periodic zero data,Croston method is a one way.It is a basically forecast strategy for products with intermittent demand.Also you can try exponential smoothing and traditional ARIMA,SARIMA models and clip the negative values in the forecast(this is according to your use case). you can find croston method in forecast package.

also refer these links as well.

https://stats.stackexchange.com/questions/8779/analysis-of-time-series-with-many-zero-values/8782 https://stats.stackexchange.com/questions/373689/forecasting-intermittent-demand-with-zeroes-in-times-series https://robjhyndman.com/papers/foresight.pdf

Upvotes: 1

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