TF-CoDiT: A New Approach to Synthesizing Treasury Futures Data

TF-CoDiT introduces a diffusion‑Transformer framework that converts multi‑channel treasury futures time series into discrete wavelet coefficients, encodes cross‑channel dependencies with a U‑shaped VAE, conditions generation on a structured FinMAP prompt, and achieves state‑of‑the‑art MSE and MAE scores across multiple contracts and horizons.

FinMAPTF-CoDiTU-VAE

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TF-CoDiT: A New Approach to Synthesizing Treasury Futures Data

Data Party THU

Apr 30, 2026 · Artificial Intelligence

Time Series Forecasting Augmentation: Frequency, Decomposition, and Patch Techniques

This article reviews why classic classification augmentations fail for forecasting, introduces the essential data‑label consistency requirement, and systematically categorizes effective time‑series augmentation methods—including frequency‑domain (RobustTAD, FreqMask, FreqMix), decomposition (STAug), and patch‑based approaches (WaveMask, WaveMix, Dominant Shuffle, Temporal Patch Shuffle)—backed by extensive experiments on long‑term, short‑term, and classification tasks.

Temporal Patch Shuffledata augmentationfrequency domain

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Time Series Forecasting Augmentation: Frequency, Decomposition, and Patch Techniques

Bighead's Algorithm Notes

Apr 27, 2026 · Artificial Intelligence

STEAM: Wavelet‑Enhanced Attention Model for Stock Price Prediction

The STEAM model combines discrete wavelet transform, a wavelet‑enhanced attention mechanism, and a market‑index prefix within a Mamba‑2 encoder to capture multi‑frequency spatial and temporal dependencies in stock data, achieving state‑of‑the‑art performance across multiple international markets as measured by IC, PnL and Sharpe ratios.

Deep LearningMamba-2attention mechanism

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STEAM: Wavelet‑Enhanced Attention Model for Stock Price Prediction