Train an LSTM neural network with time series containing seasonal and trend
I don’t know if the seasonality will be achievable in your predictions, due simply to the timeframes you we using. If you see a daily up—down movement, but only provide 20 minutes for a prediction, how will the Model know whether or not it is at a turning point? You would perhaps need to include other features that contain that information - perhaps even the time stamp would suffice.

It is odd, that the model always predicts a downward movement — I would have expected it to simply continue on the current path (up or down), assuming you have both directoins in your training data...?



Perhaps you could look into some ideas used in common timeseries analysis methods, like separating the seasonality, trend and noise and feeding them separately to the model. Search for terms SARIMA, ARIMA, seasonality and cycles. (S)ARIMA stands for “seasonal autoregressive integrated moving average”, and represents a common way to look at data over time using previous values (autoregressivej, the differences of current value to previous values (integrated) and a moving average of past time steps (moving average).

The terms generated would of course catch the phases where the value changes direction and so be able to model seasonality fairly well.