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Volatility dynamics can carry information about temporal relationships that is not apparent in the conditional mean. Extracting this information is difficult because the persistence of each variable c...
arXiv:2609.06085v1 Announce Type: cross Abstract: Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear an...
arXiv:2610.07430v1 Announce Type: new Abstract: Reducing nonstationarity in a persistent time series entails deciding how much of its temporal dependence to remove. In finance, fractional differencin...
The paper presents a Causal Graph‑Informed Temporal Convolutional Network (CG‑TCN) that fuses a learned causal graph with a temporal convolutional network to forecast retail electricity prices. By decomposing price series into multi‑resolution trends and discovering a causal graph over these components and key covariates, the model conditions its convolutions and attention on causal pathways. On ten years of Ohio residential contracts, CG‑TCN outperforms benchmarks, achieving MAEps of 3.08%, 3.82%, and 5.43% for one‑, ten‑, and fifteen‑step‑ahead forecasts, respectively.
VertiFuseX is a hybrid LSTM architecture that fuses multi‑scale temporal representations at the penultimate layer, stacking features from LSTM, Bi‑LSTM, and St‑LSTM branches and a parallel DNN stream. On 15 years of global equity index data, it reduces MAPE by 30‑54% and improves MAE and RMSE by over 40% compared to LSTM baselines, outperforming seven state‑of‑the‑art models across 33 metric‑dataset comparisons. The model is lightweight (675k parameters, 2.6 MB footprint) with 1.5 ms/sample inference latency and demonstrates robust, interpretable forecasting with reduced drawdowns in algorithmic trading simulations.
The paper introduces iCReN, a framework for causal representation learning in time‑series data that jointly models instantaneous and lagged causal relations while handling nonstationarity. It establishes theoretical conditions for identifying latent states and their causal structures up to permutation and component‑wise invertible transformations, using an auxiliary variable linked to changes in transition‑noise distributions. Experiments show that iCReN accurately recovers latent states and both types of causal relations on synthetic data and improves downstream forecasting on real‑world data.