Predict Future values(Time Series) using ARIMA

@chiggypk wrote:

I was following this article for predicting future values. Model fitted very well on training data. when i am predicting for future values, i am not getting desired results. Am i using correct parameters or need to work on it??

My code is:

import pandas as pd
import numpy as np
from statsmodels.tsa.arima_model import ARIMA
import matplotlib.pylab as plt

data_1 = pd.read_csv('AirPassengers.csv')
avg= data_1['#Passengers']
avg=list(avg)
res = pd.Series(avg, index=pd.to_datetime(data_1['Month'],format='%Y-%m'))

ts=np.log(res)
ts_diff = ts - ts.shift()
ts_diff.dropna(inplace=True)
r = ARIMA(ts,(2,1,2))
r = r.fit(disp=-1)

pred = r.predict(start='1961-01',end='1970-01')
dates = pd.date_range('1961-01','1970-01',freq='M')

predictions_ARIMA_diff = pd.Series(pred, copy=True)
predictions_ARIMA_diff_cumsum = predictions_ARIMA_diff.cumsum()
predictions_ARIMA_log = pd.Series(ts.ix[0])
predictions_ARIMA_log = predictions_ARIMA_log.add(predictions_ARIMA_diff_cumsum,fill_value=0)
predictions_ARIMA = np.exp(predictions_ARIMA_log)
plt.plot(res)
plt.plot(predictions_ARIMA)

plt.show()

print predictions_ARIMA.head()
print ts.head()

Thanks in advance

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