Plotter test completed. Now the 2D plot with markers integrated into the Plotter class within the untils.py module

The same code but with time split posterior to normalization to avoid biases and jumps between train and test sets

Old code with now reduced norms of 2 and increased sampling of 7. Bug is where train and test are separately normalized, which can lead to biases and jumps. This is addressed in the 927*lstm_time code, where it tries to first construct normalized series and then split the data in order to avoid jumps. Issue is still with the time split component, that does not work. Therefore this code is a test reference if the other 927_time code does not work.

Debugging all NaN and adding plotting directories. In this test version if everything works for 9 var 2 Norms and 6 regions

Tested different norms, and the k_scaled is the best. Trying to implement now with 9 variables using classes. Also next step to use class for ARIMA and LSTM. LSTM did not work so far...

Rewriting the notebook into class TimeSeriesExtractor. Next integrate ARIMA, or write a class for ARIMA

The LAG1 now with additional variables of Zooplanktons and Surface Incoming Radtion resluting in 9 Variables. Also some part of TimeseriesGenerator as class and functions to be implemented then fully in the Python general code together with Sampling Module

Better performance with lookback = 10 and n_forecast = 25 for ARIMA around RMSE = 6.3% and for LSTM of RMSE  = 15%. Perfect aligned peaks for LSTM and +1 lag peak shift for ARIMA

Full scale analysis for 6 variables and including the lag 1 autoregression variable Zt-1 as predictor. Transforming the data sets are required to have similar scale and range for RMSE calculation and prediction. ARIMA 12% accuracy, LSTM 2.7%. The issue with ARIMA out of phase is solved by using longer forecast steps n_steps = 25, and also LSTM is now in a better phase, if number of look back parameter increased to k_lookback = 7.

Full scale analysis for 6 variables and including the lag 1 autoregression variable Zt-1 as predictor. Transforming the data sets are required to have similar scale and range for RMSE calculation and prediction. ARIMA 10% accuracy, LSTM 7% accuracy down to 3% for longer test sequence. ARIMA is out of phase with a negative time lag....the peak occurs ahead. LSTM predicts correct the shape and phase.

Full scale analysis for 6 variables. Transforming the data sets are required to have similar scale and range for RMSE calculation and prediction. ARIMA 10% accuracy, LSTM 7% accuracy down to 3% for shorter test sequence. ARIMA is out of phase with a negative time lag....the peak occurs ahead. LSTM predicts correct the shape and phase.