I have observed that with AutoCES if I provide a ray_address, it fails to fit but when I remove ray_address it works fine. With AutoARIMA and AutoTheta, this is not an issue. They both work fine with or without using ray_address. Not sure if this is specific to my data or not. I can't share the data to replicate the error but I decided to share the anecdote.

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.