SDMixer: Sparse Dual-Mixer for Time Series Forecasting
SDMixer: Sparse Dual-Mixer for Time Series Forecasting explores SDMixer enhances time series forecasting by effectively modeling global trends and local dynamics using a dual-stream sparse Mixer.. Commercial viability score: 5/10 in Time Series Forecasting.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisDemystifying Data-Driven Probabilistic Medium-Range Weather ForecastingViability: 4.0
- Prior WorkMulti-Modal Time Series Prediction via Mixture of Modulated ExpertsViability: 5.0
- Prior WorkFrom Text to Forecasts: Bridging Modality Gap with Temporal Evolution Semantic SpaceViability: 5.0
- Higher ViabilityDualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation ModelsViability: 7.0
- Higher ViabilityEffective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression