The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

Short-term water demand forecasting provides guidance on real-time water allocation in the
water supply network, which help water utilities reduce energy cost and avoid potential …

This paper proposes an improved Arithmetic Optimization Algorithm (AOA) to train artificial
neural networks (ANNs) under dynamic environments. Despite many successful …

Many dynamic processes in the real world can be modeled as time series, so time series
prediction is significant for social and economic development. The inherent non-stationarity …

A financial market is a platform to produce data streams continuously and around 1. 145
Trillion MB of data per day. Estimation and the analysis of unknown or dynamic behaviors of …

Network monitoring data generally consists of hundreds of counters periodically collected in
the form of time-series, resulting in a complex-to-analyze multivariate time-series (MTS) …

Enhancement of the profitability of any business organization is driven by proper forecasting.
However, this is challenging as many factors affect the forecasting and the degree of …

Concept drift is a change in the joint probability distribution of the problem. This term can be
subdivided into two types: real drifts that affect the conditional probabilities p (y| x) or virtual …

Abstract Reliability estimation of Machine Learning (ML) models is becoming a crucial
subject. This is particularly the case when such models are deployed in safety-critical …

Smart meters in road lighting systems create new opportunities for automatic diagnostics of
undesirable phenomena such as lamp failures, schedule deviations, or energy theft from the …