Time Series Forecasting with Genetic Algorithms: A Novel Approach

Author(s): Shenggang Li

Originally published on Towards AI.

Exploring How Genetic Algorithms Can Adapt and Improve Time Series Predictions Beyond Traditional Methods

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Photo by Andrea De Santis on Unsplash

In my previous article on genetic algorithms, I investigated their key elements and how they function, particularly in optimization problems. If you’re interested, you can check it out here. That article focused on the mathematical foundation and practical applications of genetic algorithms.

Even though others have researched this topic (which I’ll discuss later), I’ve come up with new ideas to improve how genetic methods are used in time series forecasting. So, what if we take genetic algorithms — originally designed for optimization — and apply them to time series forecasting? That’s what I’m diving into here. Time series forecasting deals with dynamic, often unpredictable data, and I think genetic algorithms can offer a fresh approach.

While traditional forecasting methods like ARIMA or neural networks are widely used, they often lack the adaptive nature of genetic algorithms. GAs can evolve solutions over generations and adapt to patterns, which could be key in improving accuracy in time series predictions.

In this article, I’ll walk you through how I applied genetic algorithms to time series forecasting, the unique challenges that come with it, and the potential benefits.

By the end,… Read the full blog for free on Medium.

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Published via Towards AI