Applied Machine Learning Methods for Time Series Forecasting
Linsey Xiaolin Pang, Wei Liu, Lingfei Wu, Kexin Xie, Stephen D. Guo, Raghav Chalapathy, Musen Wen · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
Time series data is ubiquitous, and accurate time series forecasting is vital for many real-world application domains, including retail, healthcare, supply chain, climate science, e-commerce and economics. Forecasting, in general, has led to broad impact and a diverse range of applications. However, with large-scale, high-dimensional time-series data available, more advanced techniques must be invented or improved for highly accurate predictions. Latest data mining and machine learning techniques play a crucial role in the next generation of forecasting models. In this Applied Machine Learning Methods for Time Series Forecasting (AMLTS) workshop, we focus on effective and accurate latest machine learning approaches to solve various real-world problems. With this workshop's ability to attract audiences across various domains, we invite experienced industrial practitioners and researchers to help uncover new approaches and break new ground in time-series modelings' challenging and vital settings.