TimeAPN: Adaptive Amplitude-Phase Non-Stationarity Normalization for Time Series Forecasting
TimeAPN: Adaptive Amplitude-Phase Non-Stationarity Normalization for Time Series Forecasting explores TimeAPN is a novel framework that enhances time series forecasting by addressing non-stationarity through adaptive amplitude-phase normalization.. Commercial viability score: 7/10 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 ThisTemporal-Conditioned Normalizing Flows for Multivariate Time Series Anomaly DetectionViability: 5.0
- Builds On ThisOn the Role of Reversible Instance NormalizationViability: 2.0
- Builds On ThisForecasting Anomaly Precursors via Uncertainty-Aware Time-Series EnsemblesViability: 5.0
- Builds On ThisAnomaly detection in time-series via inductive biases in the latent space of conditional normalizing flowsViability: 4.0
- Prior WorkTaPD: Temporal-adaptive Progressive Distillation for Observation-Adaptive Trajectory Forecasting in Autonomous Driving