aisot co-organized track at Applied Machine Learning Days 2022
At the Applied Machine Learning Days 2022 at EPFL Lausanne, aisot co-presented the track “Advances...
Research
Aisot know-how is built on unique blend of multi-disciplinary research and technology.
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Temporal mixture ensemble models
"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 different markets where the exchange takes place."
Authors: Nino Antulov-Fantulin, Tian Guo, Fabrizio Lillo
Exploring Interpretable LSTM Neural Networks
"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 into the data. In this paper, we explore the structure of LSTM recurrent neural networks to learn variable-wise hidden states, with the aim to capture different dynamics in multi-variable time series and distinguish the contribution of variables to the prediction."
Authors: Tian Guo, Tao Lin, Nino Antulov-Fantulin
Sensing Social Media Signals for Cryptocurrency News
"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 news, which recently became of emerging interest to the general and financial audience."
Authors: Johannes Beck, Roberta Huang, David Lindner, Tian Guo, Ce Zhang, Dirk Helbing, and Nino Antulov-Fantulin
Impact of publicly available news on financial markets
"We quantify the propagation and absorption of large-scale publicly available news articles from the World Wide Web to financial markets. To extract publicly available information, we use the news archives from the Common Crawl, a nonprofit organization that crawls a large part of the web"
Authors: Metod Jazbec, Barna Pasztor, Felix Faltings, Nino Antulov-Fantulin and Petter N. Kolm