The curios thing is that the training data looks the same:
Although the prediction has almost the same shape as the target signal, the 1 time point difference creates a significant difference between the 2 signals.
Did anyone encountered this kind of problem and know how to solve it ? For other datasets the regression works very well, also on timeseries, also for classification, using my NN framework. It could be a bug, but why does it work very well on MNIST10 ? and other timeseries datasets ?
I used a classic RNN, not a LTSM. Input data is a time lagged input from (t, t-1, ....., t-12). Input data was normalized in [0,1], then sent to hidden layer 1 activated with tanh, then to hidden layer 2 activated with tanh, and then to error layer activated with sigmoid(tried also linear, same thing). Hidden layers each have a context layer that stores the activation of the hidden layer at the previous time t-1 to feed it at time t to the hidden layer.
I sent batches of 128 sequences of length 10,13,30,40 and other. The train dataset has about 2950 time points, and the val dataset has 155 time points.
The accuracy is: TRAIN Root mean squared error: 0.038273 TRAIN Correlation coefficient: 0.974084 VAL Root mean squared error: 0.054285 VAL Correlation coefficient: 0.861191
Does anyone know how this problem can be solved using RNNs ? I am not interested in using other statistical models or Google frameworks.
Many thanks, Viorel