LatestMachine LearningEvent-Driven Prediction: Expanding Mamba State Space Models for Conditional Forecasting 0 like January 30, 2025Share this postAuthor(s): Shenggang Li Originally published on Towards AI. A Novel Approach Combining Markov Decision Theory and Neural State Space Models for Stock Price PredictionThis member-only story is on us. Upgrade to access all of Medium.Photo by Ella Jardim on UnsplashImagine youre trying to predict stock prices, but instead of just guessing whether the price will go up or down tomorrow, youre asking a smarter question: What happens if tomorrows price crosses a certain threshold? For instance, if a stock price drops below a key support level, whats likely to happen in the following days? This kind of conditional forecasting is not only more insightful but also mirrors real-world decisions made in financial markets.The problem is that traditional time series models arent built for these what if scenarios. Thats where Markov Decision Theory meets neural state space models like Mamba to create something new. By extending the classic state space framework, we can bake future conditions like tomorrows price event directly into the prediction process. Think of it as giving the model a crystal ball, allowing it to consider not just the past but also what might happen next.In this new approach, we explore how adding event-driven dynamics to Mamba state space models unlocks exciting possibilities for forecasting. We connect these ideas to Markov Decision Processes (MDPs) to show why theyre so Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post