Pincab Passion
Vous souhaitez réagir à ce message ? Créez un compte en quelques clics ou connectez-vous pour continuer.



 
AccueilAccueil  PortailPortail  PP Official DiscordPP Official Discord  WIPs Team PP  ActivitésActivités  ÉvènementsÉvènements  S'enregistrerS'enregistrer  ConnexionConnexion  Dons  
pred685rmjavhdtoday020126 min link
pred685rmjavhdtoday020126 min link
pred685rmjavhdtoday020126 min link
pred685rmjavhdtoday020126 min link
pred685rmjavhdtoday020126 min link
pred685rmjavhdtoday020126 min link
pred685rmjavhdtoday020126 min link
pred685rmjavhdtoday020126 min link
pred685rmjavhdtoday020126 min link

Pred685rmjavhdtoday020126 Min Link (2025)

Proposed paper Title: "PRED-685: A Lightweight Timestamp-Aware Predictive Model for Short-Term Time Series Forecasting"

I’m not sure what you mean by "pred685rmjavhdtoday020126 min link." I'll assume you want an interesting paper topic and brief outline related to a predictive model or sequence that the string might hint at (e.g., "pred" = prediction, "today", a timestamp-like token). I'll propose a clear paper title, abstract, outline, and suggested experiments. pred685rmjavhdtoday020126 min link

Abstract: We introduce PRED-685, a compact neural architecture that incorporates high-resolution timestamp tokens and minimal external context to improve short-term forecasting for intermittent and noisy time series. PRED-685 combines time-aware embedding, a sparse attention mechanism tuned for sub-daily patterns, and a lightweight probabilistic output layer to provide fast, calibrated predictions suitable for on-device use. We evaluate on electricity consumption, web traffic, and delivery-log datasets, showing improved calibration and lower latency versus baseline RNN and Transformer-lite models while using ≤10 MB of model parameters. PRED-685 combines time-aware embedding

If this assumption is wrong, reply with a short correction. and delivery-log datasets