OLR-WA Online Regression with Weighted Average

TL;DR

OLR-WA is an online linear regression method that incrementally updates models using weighted averages, allowing flexible adaptation to changing data streams while reducing storage needs.

Contribution

The paper introduces OLR-WA, a novel online linear regression technique that incorporates user-defined weights for flexible adaptation to evolving data.

Findings

OLR-WA performs comparably to static models on consistent data.

OLR-WA adapts more quickly or resists change based on user-defined weights.

Experimental results demonstrate effectiveness in 2-D and 3-D scenarios.

Abstract

Machine Learning requires a large amount of training data in order to build accurate models. Sometimes the data arrives over time, requiring significant storage space and recalculating the model to account for the new data. On-line learning addresses these issues by incrementally modifying the model as data is encountered, and then discarding the data. In this study we introduce a new online linear regression approach. Our approach combines newly arriving data with a previously existing model to create a new model. The introduced model, named OLR-WA (OnLine Regression with Weighted Average) uses user-defined weights to provide flexibility in the face of changing data to bias the results in favor of old or new data. We have conducted 2-D and 3-D experiments comparing OLR-WA to a static model using the entire data set. The results show that for consistent data, OLR-WA and the static batch model perform similarly and for varying data, the user can set the OLR-WA to adapt more quickly or to resist change.