HomRR

HomRR is a hypothetical software framework described in educational materials to illustrate modular design in recurrent regression. It is not an actual project, but a case study used to discuss how homogeneous representations can be paired with recurrent models to forecast sequential data.

Overview: The central idea of HomRR is to provide a uniform representation for inputs from diverse time-series domains, followed by a recurrent regression component that maps sequences to numeric predictions. The framework emphasizes consistent preprocessing and stable training across data modalities, such as financial series, sensor readings, and event streams.

Architecture and features: HomRR envisions modular components including a data loader that ingests heterogeneous time-series, a homogeneous feature encoder that maps inputs to a shared latent space, a recurrent regression core (based on RNNs, LSTMs, or GRUs) that outputs continuous targets, and an optimization layer with regularization. It supports batch processing, early stopping, and simple plug-ins for loss functions and metrics. The design favors interpretability by exposing attention-like weights and per-timestep contributions.

Applications: In classroom exercises, HomRR is used to demonstrate end-to-end workflows for time-series forecasting, anomaly detection, and cross-domain fusion. Potential real-world uses include forecasting demand, monitoring industrial processes, and analyzing multivariate sensor data.

Status and reception: As a fictional construct, there is no official release or governance. It is primarily employed as a teaching tool to discuss modular ML architecture and the interplay between data representation and temporal modeling.

See also: Recurrent neural networks, Time-series forecasting, Machine learning frameworks, Model interpretability.