def main(): #Opdracht 1 print("OPDRACHT 1") data = pd.read_csv('data_assign_p1.csv') titels = list(data) #print(titels) #print (data['obs'][0]) X = data [['obs']] X1 = [] Y = data [['GDP_QGR']] Z = [] for i in range (0,88): X1.append(i) Z.append(data['obs'][i]) plt.plot(Z, Y, linewidth=2.0) plt.xlabel('date (1987Q2 - 2009Q1)') plt.ylabel('GDP growth rate') #ten opzichte van jaar ervoor plot_acf(Y, lags=4) #x = delay/tijdstip plot_pacf(Y, lags=4) #plot_acf(X1) plt.show() print("\n") print("OPDRACHT 2") pd.plotting.lag_plot(Y) #correlatie y(t) en y(t+1) #decomposed = seasonal_decompose(Y, model = 'additive') #decomposed.plot() model = ARMA(Y, order=(1,1)) model_fit = model.fit() print(model_fit.summary()) #data = data.join(data) model2 = AR(Y) model2_fit = model2.fit(3) #print(model_fit.summary()) print('The lag value chose is: %s' % model2_fit.k_ar) print('The coefficients of the model are:\n %s' % model2_fit.params) print(model.loglike(model_fit.params)) ypred = model2_fit.predict(Y, start = Y.index[3]) print(ypred)