Parametric system identification, which is the process of uncovering the inherent dynamics
of a system based on the model built with the observed inputs and outputs data, has been …

Electricity price prediction through statistical and machine learning techniques captures
market trends and would be a useful tool for energy traders to observe price fluctuations and …

Artificial neural networks allow the identification of black-box models. This paper proposes a
method aimed at replicating the static and dynamic behavior of a DC-DC power converter …

This paper describes how Machine Learning and Robust Optimization techniques can
greatly improve cash logistics operations. Specifically, we seek to optimize the logistics …

This paper provides the first extensive comparison of NARX and NARMAX models for multi-
step prediction. NARMAX models are more complex and require more sophisticated training …

Objective: The study focused on verifying the impact of the calendar and seasonal effects on
the accuracy of forecasts of cash withdrawals from automated teller machines (ATMs). In this …

Optimal forecasting of ATM cash repository in an optimal way is a complex task. This paper
deals with cash demand forecasting of NN5 time series data using neural networks. NN5 …

Purpose Government cash forecasting is central to achieving effective government cash
management but research in this area is scarce. The purpose of this paper is to address this …

Forecasting cash management, security, ease of use, and so on are important in the use of
Automated Teller Machine (ATM). For this purpose, in this paper, we have discussed issues …

Automated Teller Machines (ATMs) can be considered among one of the most important
service facilities in the banking industry. The investment in ATMs and the impact on the …