Research Paper

Core Innovation

This paper presents GatedLinear, which uniquely combines three complementary linear bases specialized for trend-seasonal, nonstationary drift, and phase-aligned recurrence patterns. It introduces a Tri-Factorized Fusion Gate enabling fine-grained, horizon- and phase-aware routing, outperforming prior single-backbone models with fewer parameters and explicit interpretability.

Why It Matters

Time series forecasting is critical for industries relying on accurate predictions across diverse temporal patterns. Existing models often struggle with heterogeneous data and require large, complex architectures. GatedLinear offers a more efficient, interpretable solution that adapts to varying dynamics, reducing computational costs and improving decision-making across sectors.

Potential Customers & Pain Points

• Financial institutions – Need accurate multi-horizon forecasts
• Supply chain managers – Require adaptive models for nonstationary demand
• Energy providers – Need interpretable cyclic pattern predictions
• IoT platform operators – Seek lightweight forecasting for diverse sensor data.

Business Model

SaaS platform offering API access to GatedLinear forecasting models with tiered pricing based on data volume and forecast horizons; enterprise licensing and consulting services for customization.

Competitive Landscape

• DeepAR (Amazon)
• N-BEATS
• Temporal Fusion Transformer
• Informer

Implementation Challenges

• Integration with existing forecasting pipelines
• Convincing enterprises to switch from established deep learning models
• Handling extremely large-scale or high-frequency data efficiently

Validation Strategy

• Benchmark GatedLinear against leading models on diverse real-world datasets
• Pilot deployments with financial and supply chain clients to demonstrate accuracy and cost benefits
• Collect user feedback on interpretability and integration ease