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In scenarios where obtaining real-time labels proves challenging,conventional approaches may result in sub-optimal performance. This paperpresents an optimal strategy for streaming contexts with limited labeled data,introducing an adaptive technique for unsupervised regression. The proposedmethod leverages a sparse set of initial labels and introduces an innovativedrift detection mechanism to enable dynamic model adaptations in response toevolving patterns in the data. To enhance adaptability, we integrate the ADWIN(ADaptive WINdowing) algorithm with error generalization based on Root MeanSquare Error (RMSE). ADWIN facilitates real-time drift detection, while RMSEprovides a robust measure of model prediction accuracy. This combinationenables our multivariate method to effectively navigate the challenges ofstreaming data, continuously adapting to changing patterns while maintaining ahigh level of predictive precision. We evaluate the performance of ourmultivariate method across various public datasets, comparing it tonon-adapting baselines. Through comprehensive assessments, we demonstrate thesuperior efficacy of our adaptive regression technique for tasks whereobtaining labels in real-time is a significant challenge. The resultsunderscore the method's capacity to outperform traditional approaches andhighlight its potential in scenarios characterized by label scarcity andevolving data patterns.

A Conditioned Unsupervised Regression Framework Attuned to the Dynamic Nature of Data Streams