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hey @Farzad E. Thank you for letting us know about this. Your data have particular characteristics you can disclose? for example, a lot of zeros. The

StatsForecast

class has the

fallback_model

argument to prevent the pipeline to fail: https://nixtla.github.io/statsforecast/core.html#statsforecast.

No it didn't have zeros. For that example I was using 30 time series and each had six years of weekly data. I don't want my forecasts to fallback to other models because then I wouldn't know which series used which model. But ultimately this is not an issue for me because I decided not to use Ray with StatsForecast. I now handle parallelism outside of StatsForecast. I just shared the issue here in case others see similar behavior. Basically StatsForecast might behave differently for AutoCES if ray_address is provided. Without ray_address, everything is fine.