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TimeSeries_AutoARIMA.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+# Importing necessary libraries
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+get_ipython().run_line_magic('matplotlib', 'inline')
+import warnings
+warnings.filterwarnings('ignore')
+
+
+# In[2]:
+
+
+#Installing pmdarima package
+get_ipython().system(' pip install pmdarima')
+
+
+# In[3]:
+
+
+# Importing auto_arima 
+from pmdarima.arima import auto_arima
+
+
+# In[4]:
+
+
+#Read the sales dataset
+sales_data = pd.read_csv("Champagne Sales.csv")
+
+
+# In[5]:
+
+
+sales_data.head()
+
+
+# In[6]:
+
+
+#Make sure there are no null values at the end of the dataset
+sales_data.tail()
+
+
+# In[7]:
+
+
+#Check the datatypes
+sales_data.dtypes
+
+
+# In[8]:
+
+
+#Convert the month column to datetime
+sales_data['Month']=pd.to_datetime(sales_data['Month'])
+
+
+# In[9]:
+
+
+#Recheck the datatypes
+sales_data.dtypes
+
+
+# In[10]:
+
+
+#Set the index of the Month 
+sales_data.set_index('Month',inplace=True)
+
+
+# In[11]:
+
+
+sales_data.head()
+
+
+# In[12]:
+
+
+# To understand the pattern
+sales_data.plot()
+
+
+# In[13]:
+
+
+#Testing for stationarity
+from pmdarima.arima import ADFTest
+adf_test = ADFTest(alpha = 0.05)
+adf_test.should_diff(sales_data)
+
+
+# In[14]:
+
+
+#Spliting the dataset into train and test
+train = sales_data[:85]
+test = sales_data[-20:]
+
+
+# In[15]:
+
+
+train.tail()
+
+
+# In[16]:
+
+
+test.head()
+
+
+# In[17]:
+
+
+plt.plot(train)
+plt.plot(test)
+
+
+# In[18]:
+
+
+arima_model =  auto_arima(train,start_p=0, d=1, start_q=0, 
+                          max_p=5, max_d=5, max_q=5, start_P=0, 
+                          D=1, start_Q=0, max_P=5, max_D=5,
+                          max_Q=5, m=12, seasonal=True, 
+                          error_action='warn',trace = True,
+                          supress_warnings=True,stepwise = True,
+                          random_state=20,n_fits = 50 )
+
+
+# In[19]:
+
+
+#Summary of the model
+arima_model.summary()
+
+
+# In[20]:
+
+
+prediction = pd.DataFrame(arima_model.predict(n_periods = 20),index=test.index)
+prediction.columns = ['predicted_sales']
+prediction
+
+
+# In[21]:
+
+
+plt.figure(figsize=(8,5))
+plt.plot(train,label="Training")
+plt.plot(test,label="Test")
+plt.plot(prediction,label="Predicted")
+plt.legend(loc = 'Left corner')
+plt.show()
+
+
+# In[22]:
+
+
+from sklearn.metrics import r2_score
+test['predicted_sales'] = prediction
+r2_score(test['Champagne sales'], test['predicted_sales'])
+
+
+# In[ ]:
+
+
+
+