| y_true = np.concatenate((y_train,y_test)) | y_true = np.concatenate((y_train,y_test)) | ||||
| x_cut = np.arange(x_train.shape[0]+x_test.shape[0]) | x_cut = np.arange(x_train.shape[0]+x_test.shape[0]) | ||||
| print("----- ARIMA -----") | |||||
| print() | |||||
| print("----- ARIMA Test MSE -----") | |||||
| orders_ARIMA = ["[2,1,1]","[2,2,1]","[3,1,1]","[2,1,2]"] | |||||
| for k in range(len(orders_ARIMA)): | |||||
| print("ARIMA"+orders_ARIMA[k]+" : "+str(arima_err[k])) | |||||
| print("----- DNN Test MSE -----") | |||||
| activations = ["ReLU","Swish","Sinus Cosinus","Sinus","Snake"] | |||||
| for k in range(len(activations)): | |||||
| print("DNN "+activations[k]+" : "+str(errors_test[k])) | |||||
| plt.figure() | plt.figure() | ||||
| plt.plot(x_cut,y_true,label="True data") | plt.plot(x_cut,y_true,label="True data") | ||||
| plt.plot(x,future_preds,label="Predictions") | plt.plot(x,future_preds,label="Predictions") |