Titlebook: Machine Learning and Knowledge Discovery in Databases: Research Track; European Conference, Danai Koutra,Claudia Plant,Francesco Bonchi Con

ELUDE

Deep Imbalanced Time-Series Forecasting via?Local Discrepancy Density the temporal changes appear in the training set based on LD (., estimated LD density). Our reweighting framework is applicable to existing time-series forecasting models regardless of the architectures. Through extensive experiments on 12 time-series forecasting models over eight datasets with vari

衰老

Malfunction

Sparse Transformer Hawkes Process for?Long Event Sequences applied to the time series of aggregated event counts, primarily targeting the extraction of long-term periodic dependencies. Both components complement each other and are fused together to model the conditional intensity function of a point process for future event forecasting. Experiments on real

chandel

有其法作用

Efficient Adaptive Spatial-Temporal Attention Network for?Traffic Flow Forecastingically demonstrate the validity of the extension. Furthermore, we design an adaptive spatial-temporal fusion embedding scheme to generate heterogeneous and synchronous traffic states without pre-defined graph structures. We further propose an Efficient Adaptive Spatial-Temporal Attention Network (EA

滔滔不絕地說(shuō)

Estimating Dynamic Time Warping Distance Between Time Series with?Missing Datam the same population). We show that, on multiple datasets, the proposed techniques outperform existing techniques in estimating pairwise DTW distances as well as in classification and clustering tasks based on these distances. The proposed techniques can enable many machine learning algorithms to m

glisten

Uncovering Multivariate Structural Dependency for?Analyzing Irregularly Sampled Time Seriesh network that coherently captures structural interactions, learns time-aware dependencies, and handles challenging characteristics of IS-MTS data. Specifically, we first develop a multivariate interaction module that handles the frequent missing values and adaptively extracts graph structural relat

Nostalgia

Weighted Multivariate Mean Reversion for?Online Portfolio SelectionMultivariate Mean Reversion” (WMMR) (Code is available at: .).. Empirical studies on various datasets show that WMMR has the ability to overcome the limitations of existing mean reversion algorithms and achieve superior results.

俗艷

H,-Nets: Hyper-hodge Convolutional Neural Networks for?Time-Series Forecastingces and, as a result, simultaneously extracts latent higher-order spatio-temporal dependencies. We provide theoretical foundations behind the proposed hyper-simplex-graph representation learning and validate our new Hodge-style Hyper-simplex-graph Neural Networks (H.-Nets) on 7 real world spatio-tem

灌溉

Conference proceedings 2023ptimization; Recommender Systems; Reinforcement Learning;?Representation Learning..Part V:.??Robustness; Time Series; Transfer and Multitask Learning..Part VI:.??Applied Machine Learning; Computational Social Sciences; Finance; Hardware and Systems; Healthcare & Bioinformatics; Human-Computer Intera