Good day!
I am trying to forecast for 1 day into the future with Gluon TS.
My dataset looks like this:
df: Date Volume Jan1 100 ... June1 99 June2 105 June3 90 June4 NaN
How do I forecast 1 day into the future (June4)?
I have tried the following as an example:
test_data = ListDataset([{"start": df.index[0],
"target": df.Volume[:"June4"]}],
freq="D")
estimator = NBEATSEstimator(freq="D", prediction_length=1, context_length = 5,trainer=Trainer(epochs=60,ctx="gpu"))
predictor = estimator.train(training_data=test_data)
_However, I get an error: 'Got NaN in first epoch. Try reducing initial learning rate.'__
What should I do to forecast June4 if I have all previous data available (June3 and earlier)? What am I doing wrong?
Also, If I use target June3 instead (same dataset as above with data including June3 and June4 NaN value).
test_data = ListDataset([{"start": df.index[0],
"target": df.Volume[:"June3"]}],
freq="D")
estimator = NBEATSEstimator(freq="D", prediction_length=1, context_length = 5,trainer=Trainer(epochs=60,ctx="gpu"))
predictor = estimator.train(training_data=test_data)
Forecasting results that I am getting are super close to June3 results.
Does it simply replicates June3 results, or does it use June2 and earlier and then tries to predict 1 day into the future (June3)?
