0 bookmark(s) - Sort by: Date ↓ / Title /
This article provides a roundup of notable time-series forecasting papers published between 2023 and 2024. It highlights five influential papers, including a case study from the online fashion industry, a review on forecasting reconciliation, and new deep learning models like TSMixer and CARD. The article emphasizes advancements in forecasting models, handling challenges in retail forecasting, and improvements in hierarchical forecasting methods.
Generate realistic sequential data with this easy-to-train model. This article explores using Variational Autoencoders (VAEs) to model and generate time series data. It details the specific architecture choices, like 1D convolutional layers and a seasonally dependent prior, used to capture the periodic and sequential patterns in temperature data.
First / Previous / Next / Last / Page 1 of 0