Collections of time series formed via aggregation are prevalent in many fields. These are
commonly referred to as hierarchical time series and may be constructed cross-sectionally …

Deep learning based forecasting methods have become the methods of choice in many
applications of time series prediction or forecasting often outperforming other approaches …

Recent progress in neural forecasting accelerated improvements in the performance of large-
scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. Two …

The prevalence of approaches based on gradient boosted trees among the top contestants
in the M5 competition is potentially the most eye-catching result. Tree-based methods out …

We develop a framework for forecasting multivariate data that follow known linear
constraints. This is particularly common in forecasting where some variables are aggregates …

Long-term time-series forecasting (LTTF) has become a pressing demand in many
applications, such as wind power supply planning. Transformer models have been adopted …

Mobility services require accurate demand prediction in both space and time to effectively
manage fleet rebalancing, provide quick on-demand responses, and enable advanced ride …

Time series prediction stands at the forefront of the fourth industrial revolution (Industry 4.0),
offering a crucial analytical tool for the vast data streams generated by modern industrial …

Modeling complex hierarchical and grouped feature interaction in the multivariate time
series data is indispensable to comprehend the data dynamics and predicting the future …

Demand forecasting in the online fashion industry is particularly amendable to global, data-
driven forecasting models because of the industry's set of particular challenges. These …