AMLTS 2022
Applied Machine Learning Methods for Time Series Forecasting 2022

Table of Contents

• Preface
  Summary: There were 18 papers submitted for peer-review to this workshop. Out of these, 8 papers were accepted for this volume, 5 as regular papers and 3 as short papers.

Papers

• On the Inductive Bias Transfer with Knowledge Distillation for Real-World Data
  Byeong Tak Leei, Yong-Yeon Jo, Joon-myoung Kwon
• Event2Graph: Event-driven Bipartite Graph for Multivariate Time Series Forecasting and Anomaly Detection
  Yuhang Wu, Mengting Gu, Lan Wang, Yu-san Lin, Fei Wang, Hao Yang
• Multivariate Time Series-based Solar Flare Prediction by Functional Network Embedding and Sequence Modeling
  Shah Muhammad Hamdi, Abu Fuad Ahmad, Soukaina Filali Boubrahimi
• Forecasting Multivariate Time Series of the Magnetic Field Parameters of the Solar Events
  Khaznah Alshammari, Shah Muhammad Hamdi, Ali Ahsan Muhummad Muzaheed, Soukaina Filali Boubrahimi
• GreenEyes: An Air Quality Evaluating Model based on WaveNet
  Kan Huang, Kai Zhang, Ming Liu
• Real-time Anomaly Detection for Multivariate Data Streams
  Kenneth Odoh
• Machine Learning Methods for Equity Time Series Forecasting: A Compendium
  Alberto Matuozzo, Paul D. Yoo, Alessandro Provetti, Maria H. Kim
• Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach
  Somayeh Zamani, Hamed Talebi, Gunnar Stevens