CODEWITHSUNDEEP
machine-learningtime-seriesamazon-sagemaker
museshad
museshad

Reputation: 498

Time series forecasting (DeepAR): Prediction results seem to have basic flaw

I'm using the DeepAR algorithm to forecast survey response progress with time. I want the model to predict the next 20 data points in the survey progress. Each survey is a time series in my training data. The length of each time series is the # days for which the survey ran. For example, the below series indicates that the survey started on 29-June-2011 and the last response was received on 24-Jul-2011 (25 days is the length). 

{"start":"2011-06-29 00:00:00", "target": [37, 41.2, 47.3, 56.4, 60.6, 60.6, 
61.8, 63, 63, 63, 63.6, 63.6, 64.2, 65.5, 66.1, 66.1, 66.1, 66.1, 66.1, 66.1, 
66.1, 66.1, 66.1, 66.1, 66.7], "cat": 3}

As you can see the values in the time series can remain the same or increase. The training data would never indicate a downward trend. Surprisingly, when I generated predictions, I noticed that the predictions had a downward trend. When there is no trace of downward trend in the training data, I'm wondering how the model could have possibly learned this. To me, this seems to be a basic flaw in the predictions. Can someone please throw some light on why the model might behave in this way? I build the DeepAR model with the below hyper parameters. The model was tested and the RMSE is about 9. Would it help if I change any of the hyper parameters? Any recommendations for this.

time_freq= 'D',
context_length= 30,
prediction_length= 20,
cardinality= 8,
embedding_dimension= 30,
num_cells= 40,
num_layers= 2,
likelihood= 'student-T',
epochs= 20,
mini_batch_size= 32,
learning_rate= 0.001,
dropout_rate= 0.05,
early_stopping_patience= 10

Upvotes: 2

Views: 1700

Answers (1)

vafl
vafl

Reputation: 66

If there is an up-trend in all time series, there should not be a problem learning this. If your time series usually have a rising and then falling period, then the algorithm may learn this and thus generate a similar pattern, even though the example you forecast only had an up-trend so far. How many time series do you have and how long are they on average?

All your hyper parameters look reasonable and it is a bit hard to tell, what to improve without knowing more about the data. If you don't have that many time series, you can try to increasing the number of epochs (perhaps try a few hundred) and increase early stopping to 20 - 30.

Upvotes: 2

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