Debugged ARIMA and plot

code/wilshire_5000/nn.py

@@ -15,20 +15,20 @@ def sinus_cosinus(x):
 def swish(x):
     return(x*tf.math.sigmoid(x))
 
-def arima_pred(x_train,y_test):
-    train = x_train
+def arima_pred(y_train,y_test,order=[2,1,1]):
+    train = y_train
     preds = []
     for test in range(len(y_test)):
-        model = ARIMA(train, order=(2,1,1))
+        model = ARIMA(train, order=(order[0],order[1],order[2]))
         model = model.fit()
         output = model.forecast() 
         preds.append(output[0])
         train.append(y_test[test])
-    return((np.square(preds - test)).mean())
+    return((np.square(np.array(preds) - np.array(y_test))).mean(),preds)
 
 
-activations = [tf.keras.activations.relu,swish,sinus_cosinus,sinus,snake]
-#activations = [snake]
+#activations = [tf.keras.activations.relu,swish,sinus_cosinus,sinus,snake]
+activations = [snake]
 models = []
 errors_train,errors_test = [],[]
 mean_y_train,mean_y_test,std_y_test=[],[],[]
@@ -51,7 +51,7 @@ print("----")
 print(y_test)
 x_test=x_test / maximum
 
-#print(arima_pred(list(x_train),list(y_test)))
+print(arima_pred(list(y_train),list(y_test)))
 
 
 for activation in activations :
@@ -71,7 +71,7 @@ for activation in activations :
         model.compile(optimizer=opt, loss='mse')
         model.build()
         model.summary()
-        model.fit(x_train,y_train, batch_size=1, epochs=2)
+        model.fit(x_train,y_train, batch_size=1, epochs=1)
 
         y_pred_test = model.predict(x_test)
         y_pred_train = model.predict(x_train)
@@ -91,9 +91,9 @@ for activation in activations :
 
 
 x = np.arange(9000)
-x = x / maximum
-future_preds = model.predict(x)  ## Calculated with a website the number of working days between 01-06-2020 and 01-01-2024
-    
+x_n = x / maximum
+future_preds = model.predict(x_n)  ## Calculated with a website the number of working days between 01-06-2020 and 01-01-2024
+
 
 def plot_total(x_train,y_train,y_pred_train,x_test,y_test,y_pred_test):
     x = np.concatenate((x_train,x_test))
@@ -116,7 +116,7 @@ x_cut = np.arange(df_train.shape[0]+df_test.shape[0])
 plt.figure()
 plt.plot(x_cut,y_true,label="True data")
 plt.plot(x,future_preds,label="Predictions")
-plt.xticks(range(0, 9000, 250), range(1995, 2030, 1))
+plt.xticks(range(0, 9000, 250), range(1995, 2031, 1))
 plt.xlabel("Années")
 plt.ylabel("Index Willshire5000 normalisé")
 plt.vlines([index,index+85],ymin=0,ymax=1,colors="r",label="Test Samples")