<scp>MicroAdapt:</scp> Self-Evolutionary Dynamic Modeling Algorithms for Time-evolving Data Streams

Recently, we have been inundated with dynamic, time-evolving activity data streams generated by various individual IoT/sensor devices (e.g., healthcare devices and factory equipment).What would be the ideal mechanism for adaptively summarizing and modeling individual and local activities directly on small IoT devices with limited computing resources?In this paper, we present MicroAdapt, which comprises scalable and effective algorithms for modeling and forecasting time-evolving data streams.Our proposed method has the following advantages: (a) Adaptivity: it continuously monitors the latest time-series patterns in data streams, recognizes any sudden changes, updates model parameters, and enables realtime, long-range forecasting; (b) Any-time processing: it operates on a large collection of data streams, requiring a constant time (i.e., (1)) for model updates and future predictions, at any point in time; (c) Lightweight computing: it does not require high-performance computing machines equipped with powerful GPUs, such as those used in deep learning, and can run on lightweight computers or even "edge" computing devices, such as Raspberry Pi.Intuitively, our method is inspired by the concept of an evolutionary adaptation mechanism of microorganisms, and thus it enables adaptive, any-time, lightweight model estimation and summarization of nonstationary data streams whose time-series patterns are constantly changing over time.We conduct extensive experiments on real datasets and demonstrate that MicroAdapt effectively captures important time-series patterns in the data streams, makes long-range forecasts, and consistently outperforms the existing state-of-the-art methods (e.g., TSMixer) as regards accuracy and execution speed.MicroAdapt improves accuracy by about 60% for MSE and 30% for MAE, while reducing computation time by up to six orders of magnitude (more than 100,000 times).We also applied our analytics to experiments implemented on Raspberry Pi with real IoT data streams, thus demonstrating the practicality and effectiveness of our approach.In fact, our proposed method required less than 1.95GB of memory and consumed less than 1.69W of power.