arXiv AI By Manuel Naviglio, Fabrizio Lillo

Explainable Deep Learning for Price-Trade Dynamics: From Black-Box Forecasts to Effective Parametric Models

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arXiv Machine Learning
Jun 10

Interpretable deep convolutional model for nonlinear multivariate time series in complex systems

arXiv:2501. 04339v2 Announce Type: replace-cross Abstract: We introduce the Deep Convolutional Interpreter for Time Series (DCIts), a deep-learning architecture for nonlinear multivariate time series that provides sample-specific, locally interpretable descriptions of the underlying interaction structure.

By Domjan Baric, Davor Horvatic
arXiv Machine Learning
Sep 14

VertiFuseX: Generalizable Financial Forecasting via Multi-Stream Temporal Fusion

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.

By Aashish Bohra, Vivek Vijay
arXiv Machine Learning
Oct 9

Deep Learning vs. Statistical Models for Multi-Horizon Price Forecasting of Second-Hand Electronics: A Systematic Benchmark

The paper introduces the first multi‑horizon benchmark for forecasting resale prices of second‑hand electronics, comparing eleven statistical and deep learning models on a large Polish marketplace dataset covering 100+ smartphone and laptop models from January 2022 to March 2025. Across horizons from 1 to 365 days, N‑BEATS consistently outperforms all statistical baselines, achieving an 8.51% MAPE at 365 days versus 14.94% for the best baseline—a 43% reduction—while all models converge to about 0.72% MAPE for short horizons. A single N‑BEATS model trained for the 365‑day horizon generalizes to all shorter horizons, eliminating the need for horizon‑specific models, and N‑BEATS and N‑HiTS show superior hyperparameter stability.

By Mateusz Buczy\'nski, Micha{\l} Wo\'zniak, Konrad Kaczy\'nski, Anna Wr\'oblewska, Sebastian Kuk
arXiv AI
Aug 19

MoFE: A Novel Mixture-of-Experts Framework with Fourier Neural Operators for Cryptocurrency Forecasting

MoFE is a deep learning framework that combines Fourier Neural Operators with a Mixture-of-Experts architecture to forecast cryptocurrency prices. It models volatility as a mix of multi-frequency components—including fundamental growth, mining costs, halving events, and market sentiment—using adaptive FNO and convolutional experts. Experiments on Bitcoin data from 2020 to 2025 show MoFE outperforms existing models in short‑term horizons, reducing phase‑lag errors and improving directional accuracy and information coefficient, which translates into higher Sharpe ratios in simulated trading.

By Bowen Liu, Mingming Sun
arXiv Machine Learning
Jul 21

Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables

arXiv:2603. 15802v2 Announce Type: replace Abstract: In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing.

By Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova, Malcolm Wolff, Kin G. Olivares, Ruijun Ma, Michael W. Mahoney, Andrew Gordon Wilson, Boris N. Oreshkin, Dmitry Efimov