Added ARIMA (to be debugged) and modified parser and stuff to further approach the paper

code/wilshire_5000/WILL5000INDFC2.csv

 2020-12-29,185.51
 2020-12-30,186.01
 2020-12-31,186.76
-2021-01-01,.
+2021-01-01,.

code/wilshire_5000/nn.py

 import matplotlib.pyplot as plt
 import wilshire
 import tensorflow_addons as tfa
+from statsmodels.tsa.arima.model import ARIMA
 
 def snake(x):
     return(x+(tf.math.sin(50*x)**2)/50)
 def swish(x):
     return(x*tf.math.sigmoid(x))
 
+def arima_pred(x_train,y_test):
+    train = x_train
+    preds = []
+    for test in range(len(y_test)):
+        model = ARIMA(train, order=(2,1,1))
+        model = model.fit()
+        output = model.forecast() 
+        preds.append(output[0])
+        train.append(y_test[test])
+    return((np.square(preds - test)).mean())
+
+
 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=[],[],[]
 
-df_train,df_test = wilshire.preprocess('WILL5000INDFC2.csv')
+df_train,df_test,index = wilshire.preprocess('WILL5000INDFC2.csv')
 x_train = np.arange(df_train.shape[0])
 
 
 x_test = np.arange(df_train.shape[0]+1,df_train.shape[0]+df_test.shape[0]+1)
 y_test = df_test["WILL5000INDFC"]
 y_test.to_numpy()
-
+print("----")
+print(y_test)
 x_test=x_test / maximum
 
+#print(arima_pred(list(x_train),list(y_test)))
+
+
 for activation in activations :
     y_train_5=[]
     y_test_5=[]
     errors_train_5=[]
     errors_test_5=[]
-    for k in range(2):
+    for k in range(1):
 
         model =  tf.keras.Sequential()
 
         model.compile(optimizer=opt, loss='mse')
         model.build()
         model.summary()
-        model.fit(x_train,y_train, batch_size=2, epochs=20)
+        model.fit(x_train,y_train, batch_size=1, epochs=2)
 
         y_pred_test = model.predict(x_test)
         y_pred_train = model.predict(x_train)
     plt.figure()
     plt.plot(x,y_true,label="True data")
     plt.plot(x,y_pred,label="Predictions")
-    plt.vlines([6545,6629])
+    plt.vlines([index,index+85])
     plt.legend()
     plt.show()
 
 
 print(errors_test)
 
-x=np.arange(df_train.shape[0]+df_test.shape[0]+908)
+#x=np.arange(df_train.shape[0]+df_test.shape[0]+908)
 y_true = np.concatenate((y_train,y_test))
 x_cut = np.arange(df_train.shape[0]+df_test.shape[0])
 plt.figure()
 plt.xticks(range(0, 9000, 250), range(1995, 2030, 1))
 plt.xlabel("Années")
 plt.ylabel("Index Willshire5000 normalisé")
-plt.vlines([6545,6629],ymin=0,ymax=1)
+plt.vlines([index,index+85],ymin=0,ymax=1,colors="r",label="Test Samples")
 plt.legend()
-plt.show()
+plt.show()
+
+

code/wilshire_5000/wilshire.py

 def parser(path):
     df = pd.read_csv(path,na_values='.')
     print(df.shape)
-    df = df.interpolate()
-    #df = df.dropna()
+    #df = df.interpolate()
+    df = df.dropna().reset_index(drop=True)
     print(df.shape)
     #df = df.drop(labels=np.arange(1825)) ### To obtain the same graph than in the article
     print(df.shape)
 
 def preprocess(path):
     df = parser(path)
-    print(df)
+    
     df_normalized = df[:]
     df_normalized["WILL5000INDFC"]=df_normalized["WILL5000INDFC"]/np.max(df_normalized["WILL5000INDFC"])
-
+    index_train = int(df_normalized[df_normalized["DATE"]=="2020-01-31"].index.array[0])
+    print(df)
+    print(df_normalized)
+    print(df_normalized[df_normalized["DATE"]=="2020-01-31"])
     # df.plot()
     # plt.show()
-    df_train = df_normalized[:6544]
-    df_test = df_normalized[6545:6629]
+    df_train = df_normalized[:index_train]
+    df_test = df_normalized[index_train+1:index_train+85]
 
     # df_train.plot()
     # df_test.plot()
     # plt.show()
-    return(df_train,df_test)
+    return(df_train,df_test,index_train)