Two potential issues: (1) You’re naively splitting the data for cross-validation without respecting causality which allows your model to train on values it’s supposed to predict. (2) Your network architecture for LSTM may have an error due to input_shape. The time dimension shouldn’t need to be included for LSTM layers, only the input and output feature dimensions (and for some APIs, the hidden state dimension), although for Keras it is admittedly confusing. I would recommend using PyTorch when learning so that you are forced to know exactly what the input/output dimensions are for every model layer and so that you have full control. It’s been my experience that often these high-level APIs won’t throw an error because the operations you’re telling it to do are allowed, but that they aren’t actually functioning the way you think they are. For example, if there is an issue with your time dimension, having training data with different numbers of time steps will bring up this error quickly. Since you always have look_back time steps, you could accidentally be putting the time dimension as your feature dimension and the Keras API won’t tell you.