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()
