Hybrid Attention for Binary Sequence Forecasting

Author(s): Shenggang Li

Originally published on Towards AI.

Combining n-Gram Embeddings, Count-Aware Self-Attention, and Recency-Weighted ARMA for Multi-Horizon Distributional PredictionsPhoto by Pasqualino Capobianco on Unsplash

I tackle pure binary time series forecasting by converting complex signals into 0/1 patterns — stock up/down, buy/not-buy, gene on/off — to strip out noise and reveal genuine shifts. Forecasting these sequences lets me detect market regimes, flag customer churn, and model gene-activation pathways.

My approach fuses symbolic n-gram motifs, count-aware self-attention, and recency-weighted statistics into one neural network. That hybrid lets me learn both precise short-term patterns and long-range interactions without juggling separate modules.

I call this model BinaryTrendFormer: a single, multi-task framework that predicts next-step up/down probability and the full K-step count distribution. I benchmark it using point metrics (log-loss, AUROC) and distributional scores (RPS, interval coverage), then compare my learned uncertainty intervals against simple CLT bounds.

The result is a concise, plug-and-play solution I can apply to any binary forecasting challenge — whether I’m modeling financial trends, customer journeys, gene switches, or sensor event streams.

Picture a stream of 0s and 1s flickering past. We’re betting on the very next digit — and how many 1s lie ahead. In this session, simple patterns team up with keen attention to place smarter, surer bets.

A binary time series {x_t}​ is a series of zeros and ones — machine… 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 a sponsor.

Published via Towards AI