PatchTST-based stock ranking model trained with LambdaRank loss on KRX data. Crafted by 🍡 DungiBomi
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Updated
May 1, 2025 - Jupyter Notebook
PatchTST-based stock ranking model trained with LambdaRank loss on KRX data. Crafted by 🍡 DungiBomi
Benchmarking time-series foundation models (Chronos-Bolt, zero-shot) vs. supervised (PatchTST) and classical (seasonal-naive, Croston) baselines on the M5 Walmart dataset, scored with MASE and WQL. No single model dominates: foundation/deep models win on dense SKUs, classical methods win on the intermittent tail.
Tokenization Matters: A Fair Ablation of Point-wise, and Variate-wise Transformers for Financial Time Series. (includes PatchTST, iTransformer, Crossformer, Autoformer, Fedformer, Informer, TimeNet, and non-stationarity extensions.
SOTA time-series models for industrial sensor streams · ETT, SWaT · IEEE TII
Autonomous Multi-Asset Quantitative Trading System powered by Patch Time-Series Transformers (PatchTST), PyTorch Lightning, and MetaTrader 5 (MT5) with institutional risk controllers.
chatbot designed to allow users to interact with transformer models
Heuristics-free self-supervised representation learning for time series with SIGReg (LeJEPA). Disentangles time-axis collapse, positional structure, and representation richness across PatchTST, TCN, and bag-of-patches encoders. Reproducible, seeded, significance-tested.
Probabilistic building load forecasting (Q10/Q50/Q90) + risk-aware supervisory control using Patch Transformer (QR-PatchTST)
Comparison of Return Forecasting Methods for Markowitz Portfolio Optimization: Historical Mean, AutoARIMA, PatchTST Transformer
Time-series foundation model fine-tuning toolkit with GPU acceleration
Geospatial time-series deep learning comparing PatchTST, Temporal Fusion Transformers (TFT), and RNNs on satellite NDVI imagery.
Leakage-aware time-series evaluation, conformal anomaly detection, and online replay
Electricity load forecasting pipeline for Turkish EPİAŞ consumption data with LightGBM, XGBoost, PatchTST, and weather features.
Stock price prediction comparing PatchTST transformer vs baseline models (MLP, CNN, LSTM) on S&P 500 data
Benchmark and reproducibility code for CDC-aligned influenza forecasting with time series foundation models, PatchTST, iTransformer, Chronos, TimeLLM, and MultiFoundationCore.
From-scratch CUDA implementation of memory-efficient transformer attention with up to 9.5x speedup over a naive baseline, deployed end-to-end to Raspberry Pi 4.
Disease Forecasting in a Tropical Context: A Comparative Evaluation of Model Performance and Generalizability for Dengue Fever and Influenza in Vietnam
Comparison of LSTM and PatchTST models for hourly energy consumption forecasting using TensorFlow
PatchTST 论文对齐复现:UCI 家庭负荷 L=168→H=24 直接多输出预测,滚动评估 + 泄漏审查 + 论文声称逐条核对
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