Statsmodels ARIMA:每个预测的恒定值 [英] Statsmodels ARIMA: Constant Value for Each Forecast

问题描述

我正在尝试使用 statsmodels 的 ARIMA 来预测时间序列.我正在使用 sklearn 的 TimeSeriesSplit 来评估我的模型.不幸的是，当我预测下一个数据折叠(具有真实值 Y_test)时，我得到了一个恒定的预测:

I'm trying to use statsmodels' ARIMA to forecast a time series. I'm using sklearn's TimeSeriesSplit to evaluate my models. Unfortunately, when I forecast the next fold of data (which has true value Y_test), I get a constant prediction:

if is_arima:
      Y_train = Y_train.astype(float)
      # build basic ARIMA model
      arima_model = ARIMA(Y_train, order=(2,0,1))
      # fit it, using exogenous variables
      arima_results = arima_model.fit()
      # predict next len(test) values, using exogenous variables (X_test)
      preds = arima_results.forecast(steps=len(Y_test))[0]
      print(preds)

这给了我:

115.65096239  120.89113477  121.52020239  121.59572014  121.60478583
  121.60587414  121.60600479  121.60602047  121.60602235  121.60602258
  121.6060226   121.60602261  121.60602261  121.60602261  121.60602261
  121.60602261  121.60602261  121.6060226   121.6060226   121.6060226
  121.6060226   121.6060226   121.6060226   121.6060226   121.6060226
  121.6060226   121.6060226   121.6060226   121.6060226   121.6060226...

这让我觉得我的 ARIMA 没有将时间 t 的预测用于时间 t+1 的预测?

This makes me think my ARIMA isn't using the prediction at time t for its prediction at time t+1?

我知道输出不是完全恒定的，但我的数据集显示出很大的变化，所以这有点令人担忧.知道发生了什么吗?

I understand the output isn't perfectly constant but my dataset shows large variation, so this is mildly concerning. Any idea what's going on?

谢谢！

推荐答案

您正在使用递归策略进行多步预测，即在先前步骤中生成的预测用于迭代预测下一个预测.它导致错误累积，结果预测收敛到一个值.对于很长的数据系列，Arima 表现不佳.

You are making use of recursive strategy to do multi step prediction i.e. forecasts generated in the prior steps are used for the prediction of next forecasts iteratively. It leads to error accumulation and as a result forecasting converges to a value. Arima does not perform well for very long data series.

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