# Using LSTMs for continous learning and predicting

I'm trying to develop a model to predict a commodity price movement direction based on previous observations. The model should learn common technical analysis patterns, e.g. head and shoulders. So, I think I should use a stateful LSTM so that it maintain a long term state to keep track of technical analysis patterns.

On the other side, as the data set is updated daily, i.e. new observations are added, I need the model to keep learning and making predictions every day. So, in order to update the model parameters on each new observation, I think I should use batch_size=1.

If I use the last N observations to predict the next M steps, the input tensor to the model would have the shape of (1, N, num_features):

$$X_1, X_2, ..., X_N \rightarrow Y_1, Y_2, ..., Y_M$$

Questions:

• Why should we reset_states after training on train set and prior to predication on test set? I think we shouldn't as a TA pattern may be half in the train set and half in the test set. If we reset the states, the model can't recognize this pattern.

• Can I reset_states and feed in data of a different commodity? as I only need the model to learn TA patterns not commodity specific characteristics. If so, how can I restore the states to make prediction on the first commodity?