q4.py

import pandas
from pandas import Series
from matplotlib import pyplot
from statsmodels.tsa.ar_model import AR
from sklearn.metrics import mean_squared_error
from pandas.tools.plotting import lag_plot

def imhungry():
    series = Series.from_csv('ffclean6.csv', header=0)

   # split dataset
    X = series.values
    train, test = X[1:len(X)-7], X[len(X)-7:]

   # train autoregression
    model = AR(train)
    model_fit = model.fit()
    window = model_fit.k_ar
    coef = model_fit.params

   # walk forward over time steps in test
    history = train[len(train)-window:]
    history = [history[i] for i in range(len(history))]
    predictions = list()
    for t in range(len(test)):
      length = len(history)
      lag = [history[i] for i in range(length-window,length)]
      yhat = coef[0]
      for d in range(window):
	yhat += coef[d+1] * lag[window-d-1]
      obs = test[t]
      predictions.append(yhat)
      history.append(obs)
      print('predicted=%f, expected=%f' % (yhat, obs))
    error = mean_squared_error(test, predictions)
    print('Test MSE: %.3f' % error)
    # plot
    pyplot.plot(test)
    pyplot.plot(predictions, color='red')
    pyplot.show()

def plot():
    series = Series.from_csv('daily-minimum-temperatures.csv', header=0)
    lag_plot(series)
    pyplot.show()

csvShit()