project3.py

import pandas as pd
import statsmodels.tsa.stattools as ts
import statsmodels.graphics.tsaplots
import statsmodels.api as sm
import matplotlib.pyplot as plt

def data_reader(file):
    data = pd.read_excel(file, skiprows=10)
    return data


def augdfuller():
    data = data_reader('CSUSHPISA.xls')
    print(ts.adfuller(data['CSUSHPISA'], maxlag=1))


def arima():
    data = data_reader('CSUSHPISA.xls')
    diff = data['CSUSHPISA'] - data['CSUSHPISA'].shift()
    diff = data['CSUSHPISA']
    fig = plt.figure(figsize=(12, 8))
    ax1 = fig.add_subplot(311)
    fig = statsmodels.graphics.tsaplots.plot_acf(diff, lags=200, ax=ax1)
    ax2 = fig.add_subplot(312)
    fig = statsmodels.graphics.tsaplots.plot_pacf(diff, lags=200, ax=ax2)
    ax3 = fig.add_subplot(313)
    fig = plt.plot(diff)
    plt.show()


def arma():
    from statsmodels.tsa.arima_model import _arma_predict_out_of_sample
    data = data_reader('CSUSHPISA.xls')
    diff = data['CSUSHPISA'] - data['CSUSHPISA'].shift()
    diff = data['CSUSHPISA']
    res = sm.tsa.ARMA(diff.values, (1, 0)).fit()
    params = res.params
    residuals = res.resid
    p = res.k_ar
    q = res.k_ma
    k_exog = res.k_exog
    k_trend = res.k_trend
    steps = 4

    print(_arma_predict_out_of_sample(params, steps, residuals, p, q, k_trend, k_exog, endog=diff.values, exog=None,
                                      start=len(diff.values)))


def main():
    print('Adfuller tests : \n')
    augdfuller()
    print('Arima model : \n')
    arima()
    print('Arma model : \n')
    arma_x = arma()


if __name__ == '__main__':
    main()