I am analyzing a time-series dataset using (supervised) tensorflow deep learning. The tensorflow code is given a series of inputs, and based on each input, the NN has to predict output value in near future.
For training there are lots of pre-labeled input/output of past. Each input is an array of state per time step, and each input is labeled with a result value. So far this kind of task is common at deep learning, and I used multiple GRU layers to solve this.
But problem is, I found that the pattern for finding appropriate output actually changes as time goes. For every 10 hours (approximately), the pattern changes so at every start of each pattern period (like first 1-2hours of total 10 hours) the NN is bad at predicting, but after that, prediction rate enhances. Plus, I assume the data also has some noise too.
So far my implementation with GRUs do its job, but I want to find better way to build NN if possible. I currently know some basic supervised learning techniques and some advanced like LSTM, and for unsupervised learning I know DQN and policy-gradient.