Discussing the article: "Overcoming The Limitation of Machine Learning (Part 5): A Quick Recap of Time Series Cross Validation"
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Check out the new article: Overcoming The Limitation of Machine Learning (Part 5): A Quick Recap of Time Series Cross Validation.
In this series of articles, we look at the challenges faced by algorithmic traders when deploying machine-learning-powered trading strategies. Some challenges within our community remain unseen because they demand deeper technical understanding. Today’s discussion acts as a springboard toward examining the blind spots of cross-validation in machine learning. Although often treated as routine, this step can easily produce misleading or suboptimal results if handled carelessly. This article briefly revisits the essentials of time series cross-validation to prepare us for more in-depth insight into its hidden blind spots.
In our related series of articles, we’ve covered numerous tactics on how to deal with issues created by market behavior. However, in this series, we focus on problems caused by the machine learning algorithms we wish to employ in our strategies. Many of these issues arise from the architecture of the model, the algorithms used in model selection, the loss functions we define to measure performance, and many other subjects of the same nature.
All the moving parts that collectively build a machine learning model, may unintentionally create obstacles in our pursuit of applying machine learning to algorithmic trading requiring careful diagnostic assessment. Therefore, it is important for each of us to understand these limitations and, as a community, build new solutions and define new standards for ourselves.
Machine learning models used in algorithmic trading face unique challenges, often caused by the way we validate and test them. One critical step is time series cross-validation — a method for evaluating model performance on unseen, chronologically ordered data.
Unlike standard cross-validation, time series data cannot be shuffled, as that would leak future information into the past. This makes resampling more complex and introduces unique trade-offs between bias, variance, and robustness.
In this article, we introduce cross-validation for time series, explain its role in preventing overfitting, and show how it can help train reliable models even on limited data. Using a small two-year dataset, we demonstrate how proper cross-validation improved the performance of a deep neural network compared to a simple linear model.
Author: Gamuchirai Zororo Ndawana