Abstract
In most real-world time series applications, offline modelling and forecasting methods may become ineffective and non-optimal as predictive algorithms trained on past data gets outdated in forecasting future behaviours as data might go through concept drift and evolve and a rapid rate. For the model to be adaptive, detect changes in the data stream and be fast enough to incorporate the changes required in itself before the performance degrades drastically. A lightweight adaptive model is presented in this paper which can get better result and handle concept drift seamlessly.
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Ghatage, N.B., Patil, P.D. (2023). Evolving Time Series Data Streams: A Review. In: Senjyu, T., So–In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. SMART 2023. Lecture Notes in Networks and Systems, vol 645. Springer, Singapore. https://doi.org/10.1007/978-981-99-0769-4_10
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DOI: https://doi.org/10.1007/978-981-99-0769-4_10
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