Tootfinder

Comparative Analysis of Global and Local Probabilistic Time Series Forecasting for Contiguous Spatial Demand Regions
Jiahe Ling, Wei Biao Wu
https://arxiv.org/abs/2509.08214 htt…

Comparative Analysis of Global and Local Probabilistic Time Series Forecasting for Contiguous Spatial Demand Regions
This study evaluates three probabilistic forecasting strategies using LightGBM: global pooling, cluster-level pooling, and station-level modeling across a range of scenarios, from fully homogeneous simulated data to highly heterogeneous real-world Divvy bike-share demand observed during 2023 to 2024. Clustering was performed using the K-means algorithm applied to principal component analysis transformed covariates, which included time series features, counts of nearby transportation infrastruct…

Group Formation through Game Theory and Agent-Based Modeling: Spatial Cohesion, Heterogeneity, and Resource Pooling
Chenlan Wang, Jimin Han, Diana Jue-Rajasingh
https://arxiv.org/abs/2509.18551

PoolingVQ: A VQVAE Variant for Reducing Audio Redundancy and Boosting Multi-Modal Fusion in Music Emotion Analysis
Dinghao Zou, Yicheng Gong, Xiaokang Li, Xin Cao, Sunbowen Lee
https://arxiv.org/abs/2509.11976

PoolingVQ: A VQVAE Variant for Reducing Audio Redundancy and Boosting Multi-Modal Fusion in Music Emotion Analysis
Multimodal music emotion analysis leverages audio and MIDI modalities to enhance performance. While mainstream approaches focus on complex feature extraction networks, we posit that shortening the length of audio sequence features to mitigate redundancy, especially in contrast to MIDI's compact representation, may effectively boost task performance. To achieve this, we developed PoolingVQ by combining Vector Quantized Variational Autoencoder (VQVAE) with spatial pooling, which directly compress…