我有一個(gè)時(shí)間序列，我想用它xt來預(yù)測xt + 1. 我正在使用 sklearn 的支持向量回歸，但我無法理解我在預(yù)測中發(fā)生這種轉(zhuǎn)變時(shí)做錯(cuò)了什么。這是我的代碼和結(jié)果（在圖像中）。bts_sup = timeseries_to_supervised(bts,1)bts_sup = bts_sup.iloc[1:,:]   # delete the line because x0 don't have antecedanttrain, test = split_data(bts_sup)# sacling datascaler_in = MinMaxScaler()  #  for inputsscaler_out = MinMaxScaler()  # for outputsX_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())#predictionpredicted_tr = model.predict(X_train)predicted_te = model.predict(X_test)# inverse_transform because prediction is done on scaled inputspredicted_tr = scaler_out.inverse_transform(predicted_tr.reshape(-1,1))predicted_te = scaler_out.inverse_transform(predicted_te.reshape(-1,1))#plotforcast = 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()#errorprint("MSE: ", mse(real,forcast), " R2: ", r2_score(real,forcast))print(best_model.best_params_)