activity
20182020
most citedExploring Interpretable LSTM Neural Networks over Multi-Variable Data

97 citations · 113 across the 3 of their papers we have counts for

collaborators

8 papers

q-fin.CP20204 cited

ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction

Tian Guo, Nicolas Jamet, Valentin Betrix +2

Incorporating environmental, social, and governance (ESG) considerations into systematic investments has drawn numerous attention recently. In this paper, we focus on the ESG event…

q-fin.TR2020

Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets

Nino Antulov-Fantulin, Tian Guo, Fabrizio Lillo

We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets. The predictions are built by using transaction and order book data from diffe…

cs.LG201997 cited

Exploring Interpretable LSTM Neural Networks over Multi-Variable Data

Tian Guo, Tao Lin, Nino Antulov-Fantulin

For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights i…

cs.LG2019

Low-dimensional statistical manifold embedding of directed graphs

Thorben Funke, Tian Guo, Alen Lancic +1

We propose a novel node embedding of directed graphs to statistical manifolds, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-lin…

cs.SI201912 cited

Sensing Social Media Signals for Cryptocurrency News

Johannes Beck, Roberta Huang, David Lindner +4

The ability to track and monitor relevant and important news in real-time is of crucial interest in multiple industrial sectors. In this work, we focus on the set of cryptocurrency…

cs.LG2018

Multi-variable LSTM neural network for autoregressive exogenous model

Tian Guo, Tao Lin

In this paper, we propose multi-variable LSTM capable of accurate forecasting and variable importance interpretation for time series with exogenous variables. Current attention mec…