#forecasting-models News & Analysis

2 articles tagged with #forecasting-models. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AINeutralarXiv – CS AI · Jun 106/10

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CITRAS: Covariate-Informed Transformer for Time Series Forecasting

Researchers introduce CITRAS, a Transformer-based model that improves time series forecasting by effectively integrating multiple data types: target variables, observed covariates (past-only data), and known covariates (advance-known data like calendar events). The model addresses a critical limitation in existing deep learning forecasting systems through two novel mechanisms that align future covariate information with predictions and refine cross-variable dependencies.

AIBullisharXiv – CS AI · Jun 96/10

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FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting

Researchers introduce FAME, a sparse mixture-of-experts framework that dynamically routes time series forecasting tasks to specialized models based on data characteristics. Tested on a production retail dataset with 5,000+ vending machines, the system achieves 12.4% MSE improvement over single-model baselines while using only 1.92 experts per series, demonstrating practical advantages for large-scale commercial forecasting systems.