ar model python
# create and evaluate an updated autoregressive model
from pandas import read_csv
from matplotlib import pyplot
from statsmodels.tsa.ar_model import AutoReg
from sklearn.metrics import mean_squared_error
from math import sqrt
# load dataset
series = read_csv('daily-minimum-temperatures.csv', header=0, index_col=0, parse_dates=True, squeeze=True)
# split dataset
X = series.values
train, test = X[1:len(X)-7], X[len(X)-7:]
# train autoregression
window = 29
model = AutoReg(train, lags=29)
model_fit = model.fit()
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))
rmse = sqrt(mean_squared_error(test, predictions))
print('Test RMSE: %.3f' % rmse)
# plot
pyplot.plot(test)
pyplot.plot(predictions, color='red')
pyplot.show()