У меня есть временной ряд, и я хотел бы использовать xt для прогнозирования xt + 1. Я использую векторную регрессию sklearn Support, но я не могу понять, что я сделал неправильно, чтобы иметь такой сдвиг в моих прогнозах. Вот мой код и результат (на картинке).

bts_sup = timeseries_to_supervised(bts,1)
bts_sup = bts_sup.iloc[1:,:]   # delete the line because x0 don't have antecedant
train, test = split_data(bts_sup)

# sacling data
scaler_in = MinMaxScaler()  #  for inputs
scaler_out = MinMaxScaler()  # for outputs

X_train = scaler_in.fit_transform(train[:,0].reshape(-1,1))
y_train = scaler_out.fit_transform(train[:,1].reshape(-1,1))

X_test = scaler_in.transform(test[:,0].reshape(-1,1))
y_test = scaler_out.transform(test[:,1].reshape(-1,1))


param_grid = {"C": np.linspace(10**(-2),10**3,100),
             'gamma': np.linspace(0.0001,1,20)}

mod = SVR(epsilon = 0.1,kernel='rbf')
model = GridSearchCV(estimator = mod, param_grid = param_grid,
                                   scoring = "neg_mean_squared_error",verbose = 0)

best_model = model.fit(X_train, y_train.ravel())

#prediction
predicted_tr = model.predict(X_train)
predicted_te = model.predict(X_test)

# inverse_transform because prediction is done on scaled inputs
predicted_tr = scaler_out.inverse_transform(predicted_tr.reshape(-1,1))
predicted_te = scaler_out.inverse_transform(predicted_te.reshape(-1,1))

#plot
forcast = np.concatenate((predicted_tr,predicted_te))
real = np.concatenate((train[:,1],test[:,1]))
plt.plot(real, color = 'blue', label = 'Real Erlangs')
plt.plot(forcast,"--", linewidth=2,color = 'red', label = 'Predicted Erlangs')
plt.title('Erlangs Prediction--'+data_set.columns[choice])
plt.xlabel('Time')
plt.ylabel('Erlangs')
plt.legend()
plt.show()


#error
print("MSE: ", mse(real,forcast), " R2: ", r2_score(real,forcast))
print(best_model.best_params_)

поезд

[[9,26 11,01] [11,01 22,72] [22,72 20,75] [20,75 11,54] [11,54 11,85] [11,85 18,17] [18,17 16,05] [16,05 17,98] [17,98 14,85] [14,85 12,62] [12,62 16,95] [16,95 16,81] [ 16,81 16,23] [16,23 21,81] [21,81 22,47] [22,47 20,37] [20,37 16,68] [16,68 17,07] [17,07 20,48] [20,48 21,99] [21,99 25,54] [25,54 21,1] [21,1 16,91] [16,91 24,23] [24,23 27,37] ] [27,37 30,55] [30,55 28,47] [28,47 26,74] [26,74 40,37] [40,37 36,55] [36,55 39,65] [39,65 45,58] [45,58 48,91] [48,91 37,82] [37,82 39,7] [39,7 36,09] [36,09 25,33] [36,09 25,33] [ 25,33 23,64] [23,64 18,33] [18,33 21,59] [21,59 22,4] [22,4 15,89] [15,89 18,94] [18,94 21,78] [21,78 19,38] [19,38 17,81] [17,81 21,33] [21,33 22,61] [22,61 27,11] [27,11,4 ] [26,48 19,87] [19,87 18,57] [18,57 14,03] [14,03 18,82] [18,82 22,46] [22,46 22,33] [22,33 21,58] [21,58 22,66] [22,66 19,51] [19,51 21,54] [21,54 20,58] [20,58 20,48] [20,58 20,48]

тестовое задание

[[20,48 25,78] [25,78 21,89] [21,89 19,61] [19,61 22,95] [22,95 21,67] [21,67 26,03] [26,03 21,96] [21,96 21,81] [21,81 21,91] [21,91 21,82] [21,82 19,6] [19,6 24,61] [19,6 24,61] [19,6 24,61] [19,6 24,61] [19,6 24,61] [19,6 24,61] [ 24,61 30,97] [30,97 18,29] [18,29 19,84] [19,84 20,81] [20,81 29,17] [29,17 24,01] [24,01 21,3] [21,3 25,08] [25,08 27,18] [27,18 26,59] [26,59 25,99] [25,99 28,74] [28,74 25,32 ] [25,32 27,56] [27,56 28,69]]