ZengyuCao-ARTC
commented
1 month ago Regarding model selection mechanism or representation learning to save computational resources, to share some literature study from my side. Source github issue: [Prep] Literature Review · Issue #2 · artc-dsc/AI-FusionCast-Data-Processing-Service
Feature Extraction Characteristic-Based-Clustering-for-Time-Series-Data.pdf Year 2006 Rule induction for forecasting method selection: meta-learning the characteristics of univariate time series Framework DFSeer: A Visual Analytics Approach to Facilitate Model Selection for Demand Forecasting 2020 AutoAI-TS: AutoAI for Time Series Forecasting Year 2021 AutoForecast: Automatic Time-Series Forecasting Model Selection year 2022 SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting year 2023 UniTS: Building a Unified Time Series Model (arxiv.org) year 2024 (add Doris's found latest one) Retail Demand Forecast model selection in retail demand forecasting Year 2022 https://www-sciencedirect-com.libproxy1.nus.edu.sg/science/article/pii/S0169207021000935 retail_forecasting_review_180924_v9.21.pdf Analytics for an Online Retailer - Demand Forecasting and Price Optimization at Rue La La.pdf Demand forecasting for fashion products: A systematic review Year 2016 Meta-learning Meta-learning how to forecast time series year 2022 Meta-Learning: A Survey Time series model selection with a meta-learning approach
ARTC-Doris
@namtuanle as suggested in email, please review and share finding in next bi-weekly meeting. Paper from IHPC on “Combined Algorithm Selection and Hyperparameter Optimization”. IHPC_Optimizing Demand Forecasting A Framework With Bayesian Optimization Embedded Reinforcement Learning f[1].pdf
You can upload your slides to Forecasting Engine Folder.