Keras multivariate time series forecasting model returns NaN as MAE and loss-CodePudding

I have multivariate time series data, collected every 5 seconds for a few days. This includes columns of standardized data, which looks like below (few example values). "P1" is the label-column.

|-------|-----------------------|-----------------------|----------------------|-----------------------|-----------------------|------------------------|------------------------|----------------------|----------------------|
|       | P1                    | P2                    | P3                   | AI_T_MOWA             | AI_T_OEL              | AI_T_KAT_EIN           | AI_T_KAT_AUS           | P-Oel                | P-Motorwasser        |
|-------|-----------------------|-----------------------|----------------------|-----------------------|-----------------------|------------------------|------------------------|----------------------|----------------------|
| 0     | 0.8631193380009695    | 0.8964414887167506    | 0.8840858759128901   | -0.523186057460264    | -0.6599697679790338   | 0.8195843978382326     | 0.6536355179773343     | 2.0167991331023862   | 1.966765280217274    |
|-------|-----------------------|-----------------------|----------------------|-----------------------|-----------------------|------------------------|------------------------|----------------------|----------------------|
| 1     | 2.375731412346451     | 2.416190921505275     | 2.3921080971495456   | 1.2838015319452019    | 0.6783070711474897    | 2.204838829646018      | 2.250184559609546      | 2.752702514412287    | 2.7863834647854797   |
|-------|-----------------------|-----------------------|----------------------|-----------------------|-----------------------|------------------------|------------------------|----------------------|----------------------|
| 2     | 2.375731412346451     | 2.416190921505275     | 2.3921080971495456   | 1.2838015319452019    | 1.2914092683827934    | 2.2484584825559955     | 2.2968465552769324     | 2.4571347629025726   | 2.743245665597679    |
|-------|-----------------------|-----------------------|----------------------|-----------------------|-----------------------|------------------------|------------------------|----------------------|----------------------|
| 3     | 2.3933199248388406    | 2.416190921505275     | 2.3753522946913606   | 1.2838015319452019    | 1.5485166414169536    | 2.2557284247076588     | 2.3039344533529906     | 2.31839887954087     | 2.7863834647854797   |
|-------|-----------------------|-----------------------|----------------------|-----------------------|-----------------------|------------------------|------------------------|----------------------|----------------------|

Corresponding graphs of the standardized data show nothing out of the ordinary.

I have split this data into train, validation and test sets, so that my training data is the first 70% of overall data, the validation are the next 20% and the test are the last 10%.

train_df_st = df[0:int(self._n*0.7)]
val_df_st = df[int(self._n*0.7):int(self._n*0.9)]
test_df_st = df[int(self._n*0.9):]

I then generate windows through the WindowGenerator class from tensorflows tutorial like

I have tried implementing yet another model (LSTM) with slightly different windows, but a similar approach, but I get the same NaN's, so I believe it is not my models problem, but something in my data?.

CodePudding user response：

Turns out my standarization of the data was faulty, normalizing it, I get actual values instead of NaN.