activity
20192025
most citedLOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Robust Causal Discovery in Real-World Time Series with Power-Laws

Matteo Tusoni, Giuseppe Masi, Andrea Coletta +3

Exploring causal relationships in stochastic time series is a challenging yet crucial task with a vast range of applications, including finance, economics, neuroscience, and climat…

q-fin.TR20231 cited

LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study

Matteo Prata, Giuseppe Masi, Leonardo Berti +6

The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL mod…

cs.NI2021

Static and Dynamic Failure Localization through Progressive Network Tomography

Viviana Arrigoni, Novella Bartolini, Annalisa Massini +1

We aim at assessing the states of the nodes in a network by means of end-to-end monitoring paths. The contribution of this paper is twofold. First, we consider a static failure sce…

cs.NI2019

On Fundamental Bounds of Failure Identifiability by Boolean Network Tomography

Novella Bartolini, Ting He, Viviana Arrigoni +2

Boolean network tomography is a powerful tool to infer the state (working/failed) of individual nodes from path-level measurements obtained by egde-nodes. We consider the problem o…

cs.DC2019

Fast Strassen-based Parallel Multiplication

Viviana Arrigoni, Annalisa Massini

Matrix multiplication appears as intermediate operation during the solution of a wide set of problems. In this paper, we propose a new cache-oblivious algorithm for the $A^…