Predicting Nan with Stock Data (regardless of model)

Hi there,

running into trouble with predicting Nan Values. Initially thought that this could be from using a weekday timeseries (working with stock data). I saw the post about changing Freq=‘B’ for a business day time index. Even with doing this, the prediction array still has Nan values. I tried using this data with NBEATs as well as RNN model - both have yielded the same results. Would love some help!

Below is the data I’m using as well as the prediction array readout.

 close
timestamp             
1999-11-19  142.500000
1999-11-22  142.468704
1999-11-23  141.218704
1999-11-24  141.968704
1999-11-26  141.437500
1999-11-29  140.937500
1999-11-30  139.281204
1999-12-01  140.406204
1999-12-02  141.250000
1999-12-03  143.843704

[5589 rows x 1 columns]
100%|██████████| 100/100 [27:40<00:00, 16.60s/it]
100%|██████████| 229/229 [00:09<00:00, 24.62it/s]
<TimeSeries (DataArray) (time: 229, component: 1, sample: 1)>
array([[[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

...

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]],

       [[nan]]])
Coordinates:
  * time       (time) datetime64[ns] 2017-09-11 2017-09-18 ... 2022-01-24
  * component  (component) <U1 '0'
Dimensions without coordinates: sample

[MRV1N2]

Hello orthosku, I’ve also been dealing with darts for a few days to predict Stock Prices, would you like to exchange experience, my Discord name is MRV1N#1905

[orthosku]

Makes sense, thank you! So in this use case, weekday holidays would marked as Nan by the ts object; these values would be interpolated.