使用窗口数据集预测未来期间的时间序列中的值

Question

我拥有直到3650个时间步长的数据，但我想做出未来的预测，即3650个时间步长之后的数据。我是机器学习的新手，显然无法弄清楚。我该怎么做？以供参考，Colab Notebook

Answer 1

[如果您有兴趣，我们正在开发一个工具箱，它针对这些用例扩展了scikit-learn。因此，使用sktime，您可以简单地编写：

import numpy as np
from sktime.datasets import load_airline
from sktime.forecasting.compose import ReducedRegressionForecaster
from sklearn.svm import RandomForestRegressor
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.performance_metrics.forecasting import smape_loss

y = load_airline()  # load 1-dimensional time series
y_train, y_test = temporal_train_test_split(y)  
fh = np.arange(1, len(y_test) + 1)  # forecasting horizon
regressor = RandomForestRegressor()  
forecaster = ReducedRegressionForecaster(regressor, window_length=10)
forecaster.fit(y_train)
y_pred = forecaster.predict(fh)
print(smape_loss(y_test, y_pred))
>>> 0.139046791779424