ARIMA model commit

arima.py

+import pandas as pd
+import numpy as np
+from statsmodels.tsa.stattools import adfuller
+from pmdarima import auto_arima
+from statsmodels.tsa.arima_model import ARIMA
+from sklearn.metrics import mean_squared_error
+from math import sqrt
+
+import warnings
+warnings.filterwarnings("ignore")
+
+df = pd.read_csv('processed_data/nikewaratiya_train.csv', index_col='ds', parse_dates=True)
+df = df.dropna()
+print('Shape of data', df.shape)
+print(df.head())
+
+
+def adf_test(dataset):
+    dftest = adfuller(dataset, autolag='AIC')
+    print("1. ADF : ", dftest[0])
+    print("2. P-Value : ", dftest[1])
+    print("3. Num Of Lags : ", dftest[2])
+    print("4. Num Of Observations Used For ADF Regression and Critical Values Calculation :", dftest[3])
+    print("5. Critical Values :")
+    for key, val in dftest[4].items():
+        print("\t", key, ": ", val)
+
+
+adf_test(df['y'])
+
+stepwise_fit = auto_arima(df['y'], suppress_warnings=True)
+
+print(stepwise_fit.summary())
+print(df.shape)
+train = df.iloc[:-30]
+test = df.iloc[-30:]
+print(train.shape, test.shape)
+print(test.iloc[0], test.iloc[-1])
+
+model = ARIMA(train['y'], order=(1, 0, 0))
+model = model.fit()
+model.summary()
+
+start = len(train)
+end = len(train) + len(test) - 1
+
+pred = model.predict(start=start, end=end, typ='y').rename('ARIMA predictions')
+# pred.index=index_future_dates
+pred.plot(legend=True)
+test['y'].plot(legend=True)
+
+rmse = sqrt(mean_squared_error(pred, test['y']))
+print(rmse)
+
+model2 = ARIMA(df['y'], order=(2, 1, 1))
+model2 = model2.fit()
+df.tail()
+
+# model2.save('model.pkl')