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信息论和集成模型

2026年7月8日 01:57 33 Comments
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我们应该如何更好地集成时间序列预测？《信息论和集成模型》一文首先出现在《走向数据科学》上。

来源:走向数据科学

Coming out of the pandemic, statisticians have run into a slew of geo-political complications further augmenting the inability to forecast business variables with accuracy.乌克兰在 2022 年推高了零售价格，还是 2021 年的量化宽松才是罪魁祸首？有些模型说的是一回事，有些模型说的是另一回事，因此很难准确预测通货膨胀。

At the very foundation of econometrics, we rely on minimizing distance (MSE, RMSE, etc) between two points (forecast, actual) in the same domain (time, frequency). All of these have served the community immensely to improve the accuracy of forecasts.这些指标受欢迎有以下几个原因：

  • 它们是非参数的。 Meaning models developed using different assumptions and structures can all be compared since the end output is the same – accuracy.
  • Historically, it has been true that these metrics offer enough variation in residual distribution for us to classify different models and cluster their performance. For instance, they help us answer if a class of ARMA models better suits the data or a class of state space models.更一般地说，它有助于对最佳拟合算法进行分类。
  • Often, they sit in euclidean geometries and have nice properties that make building newer and more sophisticated methods on top of them much easier.测量拓扑不变。 This unlocks the potential for making transformations and representations of the data, the model, or both to extract deeper relationships.现代机器学习模型是通过利用这一关键属性来构建的。 WLOG, we’ll focus on simple econometric models for the purposes of this article.

    • These benefits have helped improve statistical packages since the late 40s so much so that today we have more general packages that are able to select best fit models without the user ever having to assume a structure.

    那么我们该怎么办呢？

    潜在的解决方案可以来自 2 种不同的路径：

    模型集成

    回到第一点。

    信息论

    利用 these 参数的 回到 since 集成 原因 accuracy 不同的 模型 us improve 潜在的 拟合 机器学习 same 属性 受欢迎 models 关键 算法 different forecast 信息论