9 citations · 27 across the 11 of their papers we have counts for
12 papers
Continuous-time convolutions model of event sequences
Vladislav Zhuzhel, Vsevolod Grabar, Galina Boeva +9
Event sequences often emerge in data mining. Modeling these sequences presents two main challenges: methodological and computational. Methodologically, event sequences are non-unif…
Adversarial Attacks on Deep Models for Financial Transaction Records
Ivan Fursov, Matvey Morozov, Nina Kaploukhaya +7
Machine learning models using transaction records as inputs are popular among financial institutions. The most efficient models use deep-learning architectures similar to those in…
COHORTNEY: Non-Parametric Clustering of Event Sequences
Vladislav Zhuzhel, Rodrigo Rivera-Castro, Nina Kaploukhaya +3
Cohort analysis is a pervasive activity in web analytics. One divides users into groups according to specific criteria and tracks their behavior over time. Despite its extensive us…
TOTOPO: Classifying univariate and multivariate time series with Topological Data Analysis
Polina Pilyugina, Rodrigo Rivera-Castro, Eugeny Burnaev
This work is devoted to a comprehensive analysis of topological data analysis fortime series classification. Previous works have significant shortcomings, such aslack of large-scal…
Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting
Rodrigo Rivera-Castro, Aleksandr Pletnev, Polina Pilyugina +4
Topological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure. Its use for time series data has been limited. In thi…
Graph Neural Networks for Model Recommendation using Time Series Data
Aleksandr Pletnev, Rodrigo Rivera-Castro, Evgeny Burnaev
Time series prediction aims to predict future values to help stakeholders make proper strategic decisions. This problem is relevant in all industries and areas, ranging from financ…
Topological Data Analysis for Portfolio Management of Cryptocurrencies
Rodrigo Rivera-Castro, Polina Pilyugina, Evgeny Burnaev
Portfolio management is essential for any investment decision. Yet, traditional methods in the literature are ill-suited for the characteristics and dynamics of cryptocurrencies. T…
An industry case of large-scale demand forecasting of hierarchical components
Rodrigo Rivera-Castro, Ivan Nazarov, Yuke Xiang +3
Demand forecasting of hierarchical components is essential in manufacturing. However, its discussion in the machine-learning literature has been limited, and judgemental forecasts…
Addressing Cold Start in Recommender Systems with Hierarchical Graph Neural Networks
Ivan Maksimov, Rodrigo Rivera-Castro, Evgeny Burnaev
Recommender systems have become an essential instrument in a wide range of industries to personalize the user experience. A significant issue that has captured both researchers' an…
DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data
Aiusha Sangadiev, Rodrigo Rivera-Castro, Kirill Stepanov +5
This work proposes DeepFolio, a new model for deep portfolio management based on data from limit order books (LOB). DeepFolio solves problems found in the state-of-the-art for LOB…
Topological Data Analysis of Time Series Data for B2B Customer Relationship Management
Rodrigo Rivera-Castro, Polina Pilyugina, Alexander Pletnev +3
Topological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure. Its use for time series data has been limited to the…
Demand forecasting techniques for build-to-order lean manufacturing supply chains
Rodrigo Rivera-Castro, Ivan Nazarov, Yuke Xiang +3
Build-to-order (BTO) supply chains have become common-place in industries such as electronics, automotive and fashion. They enable building products based on individual requirement…