arima.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 29 16:15:47 2017

@author: rakshitanagalla
"""

import numpy as np
import pandas as pd
from statsmodels.tsa.arima_model import ARIMA
import datetime as dt
from dateutil.relativedelta import relativedelta
import calendar

def add_months(sourcedate,months):
    month = sourcedate.month - 1 + months
    year = int(sourcedate.year + month / 12 )
    month = month % 12 + 1
    day = min(sourcedate.day,calendar.monthrange(year,month)[1])
    return dt.date(year,month,day)


data = pd.read_csv('Recent_toBeUsed.csv')
data.drop(['ln_e','YearMonth','Unnamed: 4', 'y', 'y_US','m_in_billion_rupees', 'm_US_in billion rupees','m_in_billion_rupees.1','i', 'i_US','m_US_in billion', 'Dummy', 'Dummy2'],inplace=True,axis=1)
DTTMFormat = '%d-%m-%Y'
data['DTTM'] = pd.to_datetime(data['DTTM'],format=DTTMFormat)
data.set_index('DTTM', inplace = 'True')
#data_diff = data.diff().dropna() #differencing


ForecastTime = dt.datetime.strptime('1-7-2006', DTTMFormat)

#Choose lag length
#model.select_order(8) # lag = 1 best
lag = 1

cnt =0
outputPredDF = pd.DataFrame()
se = pd.DataFrame()
while cnt < 110:
    train_period = [ForecastTime - relativedelta(years=10), add_months(ForecastTime,-1)]
    if cnt == 0:
        train = data[add_months(train_period[0],1):train_period[1]]
    else:
        train = data[train_period[0]:train_period[1]]
    test = data[ForecastTime:add_months(ForecastTime+relativedelta(years=1),-1)]
    
    # make an ARIMA model
    model = ARIMA(train['e'], order=(2, 1, 2))  #(p,d,q)
    model_fit = model.fit(disp=0) 

    #forecast
    n_steps_ahead = 12
    final_pred = model_fit.forecast(steps = 12)[0]

    #save predictions and errors 
    columName = str(ForecastTime.year)+'M'+str(ForecastTime.month)
    outputPredDF[columName+'_pred'] = final_pred
    se[columName+'_err'] = np.array((test.e - final_pred)**2)
      
    cnt +=1
    ForecastTime = add_months(ForecastTime,1)
    print(train_period)

rmse = np.sqrt(se.mean(axis = 1)) #T_0 = 110