dpp.DataStates[i] = Data(dpp.outlier_treatment(dpp.DataStates['Standardized_data'].df,forecastMeasure,outlier_type=i),forecastMeasure,m_count,m_perc,i_count,i_perc) dpp.imputation(dpp.DataStates[i].df,forecastMeasure,outlier_type=i) l = set(dpp.DataStates.keys()) - set(['Standardized_data','Normal_Distribution','boxplot']) d = {'Dataset':[],'Predictions':[]} for i in range(n_preds): d['t+'+str(i+1)+' RMSE'] = list() for i in l: rnn = RNN(dpp.DataStates[i].df.copy(),forecastMeasure,n_lag,n_preds,nvars) x, y, scaler = rnn.prepare_data(dpp.DataStates[i].df.copy()) x_train,x_test = rnn.train_test_split(x,test_size=0.3) y_train,y_test = rnn.train_test_split(y,test_size=0.3) model = rnn.fit([10,10],x_train, y_train) forecasts = rnn.forecast(model, x_test) forecasts = rnn.inverse_transform(forecasts, scaler) actual = rnn.inverse_transform(y_test.values, scaler) rmse = rnn.evaluate_forecasts(y_test.values, forecasts) d['Dataset'].append(i) for i in range(n_preds): d['t+'+str(i+1)+' RMSE'].append(rmse[i]) d['Predictions'].append(forecasts) fdf = pd.DataFrame(d) n = fdf[fdf['t+1 RMSE']==min(fdf['t+1 RMSE'])]['Dataset'].item() rnn = RNN(dpp.DataStates[n].df.copy(),forecastMeasure,n_lag,n_preds,nvars) x, y, scaler = rnn.prepare_data(dpp.DataStates[n].df.copy()) x_train,x_test = rnn.train_test_split(x,test_size=0.7) y_train,y_test = rnn.train_test_split(y,test_size=0.7) k = list(x_test.iloc[-1,:]) + list(y_test.iloc[-1,:])