Abstract This work addresses the challenges of learning from Evolving Streaming Time Series, a scenario defined in streaming contexts by the need for continuous learning, managing temporal dependence, reacting to concept drifts, and avoiding catastrophic forgetting. Streaming Continual Learning (SCL) is an emerging paradigm specifically aimed at tackling these issues. To this end, SCL combines elements of Streaming Machine Learning, Continual Learning, and Time Series Analysis. The core idea of SCL is to maintain a progressively enriched representation that reflects everything learned so far. When a drift occurs, the model integrates newly emerging information and also selectively draws on earlier knowledge. In this context, the present work proposes Dynamic Continuous Progressive Neural Networks (DYNcPNN), a comprehensive and pioneering SCL embodiment built on our previously introduced cPNN architecture. DYNcPNN is a dynamic variant designed to adapt more effectively to concept drifts. Its key contribution is a mechanism that dynamically determines when to expand the architecture to incorporate new knowledge, reducing unnecessary complexity while preserving high performance. DYNcPNN also introduces a strategy to prevent forgetting when, after a concept drift, the model continues learning without expanding, thereby risking overwriting the existing knowledge. The model integrates an automatic concept drift detection system that enables adaptation without manual intervention. Experimental results show that DYNcPNN consistently outperforms traditional SML models, cPNN, and a continuously trained LSTM. It adapts more quickly to concept drifts, effectively mitigates catastrophic forgetting, and optimizes memory usage. Moreover, the results highlight the limitations of SML models, which are unable to account for temporal dependence.

In batch learning, it is commonly assumed that samples are independent and identically distributed (i.i.d.). However, this assumption does not hold in dynamic environments where data streams are not identically distributed due to concept drifts. Furthermore, while most Streaming Machine Learning (SML) literature assumes independence among examples, data streams often have important temporal components that learning should adequately consider. Neglecting this temporal dependence can lead to significant misguidance in designing and evaluating SML models. To support our thesis, we propose Tenet, a novel benchmarking framework designed to evaluate data stream classifiers in non-i.i.d. scenarios comparatively. Tenet consists of a data stream generator and a baseline. The data stream generator introduces temporal dependence into the data streams commonly used for evaluating SML algorithms. The baseline is a continuous version of the Long Short-Term Memory algorithm called cLSTM. Extensive experiments using Tenet demonstrate that cLSTM consistently outperforms state-of-the-art SML classifiers when learning from data streams with temporal dependence. This result is a call to action for the SML and the Deep Learning communities to investigate classifiers in the time-dependent streaming scenario and makes Tenet the first publicly available benchmark to support this research.

Sports analytics has grown significantly through continuous data streams from wearable tracking devices. This article explores the performance of traditional Machine Learning methods (ML) compared to Streaming Machine Learning approaches (SML) in the context of sports analytics, specifically in identifying passes during a soccer match. The study utilizes leg movement data from wearable sensors on players’ shoes to differentiate between pass and nonpass actions. Balanced, imbalanced and rebalanced datasets are created and analyzed. Several traditional and streaming algorithms are tested, and statistical analyses are performed to assess their performance. The findings indicate that Streaming Machine Learning can achieve comparable or better performance than traditional methods, especially on larger rebalanced datasets. This research highlights the potential of Streaming Machine Learning for online sports analytics and suggests future directions for exploring player tendencies and edge device adaptability.

Abstract Dealing with an unbounded data stream involves overcoming the assumption that data is identically distributed and independent. A data stream can, in fact, exhibit temporal dependencies (i.e., be a time series), and data can change distribution over time (concept drift). The two problems are deeply discussed, and existing solutions address them separately: a joint solution is absent. In addition, learning multiple concepts implies remembering the past (a.k.a. avoiding catastrophic forgetting in Neural Networks’ terminology). This work proposes Continuous Progressive Neural Networks (cPNN), a solution that tames concept drifts, handles temporal dependencies, and bypasses catastrophic forgetting. cPNN is a continuous version of Progressive Neural Networks, a methodology for remembering old concepts and transferring past knowledge to fit the new concepts quickly. We base our method on Recurrent Neural Networks and exploit the Stochastic Gradient Descent applied to data streams with temporal dependencies. Results of an ablation study show a quick adaptation of cPNN to new concepts and robustness to drifts.

Processing data streams gained much importance in recent years. Standard machine learning algorithms do not cope well with non-stationary streaming data, where decision models evolve and generate so-called concept drift. Online adaptive algorithms emerged to solve these issues. They learn incrementally and generally require explicit forgetting mechanisms to adapt to concept drift. In this paper, we propose the application of Kalman filtering to handle evolving data streams. This novel approach addresses data stream mining and concept drift management challenges from a new perspective, directly modelling a representation suitable for the data streams. First, we study a Kalman filter based learning a pproach and investigate its integration into the Naïve Bayes algorithm, namely KalmanNB. Additionally, we propose the Hoeffding Kalman Tree, a combination of the Hoeffding Tree with KalmanNB. Empirical results demonstrate that the Kalman filter based approach inherently manages concept drifts, and it adapts to the emerging concept more rapidly than the state-of-the-art algorithms. Moreover, it is an accurate and robust approach and requires less storage while still being faster.