TopoPrimer: The Missing Topological Context in Forecasting Models

Apple researchers have introduced TopoPrimer, a framework that incorporates the global topological structure of time-series data as an explicit input for forecasting models. By accounting for the relationships between different data patterns, this method helps stabilize predictions during demand volatility and improves accuracy for new series with limited historical data.