ITISE 2025 - 20 articles
Canaria, Spain | 16–18 July 2025
Volume Editors:
Olga Valenzuela, University of Granada, Granada, Spain
Fernando Rojas, University of Granada, Granada, Spain
Luis Javier Herrera, University of Granada, Granada, Spain
Hector Pomares, University of Granada, Granada, Spain
Ignacio Rojas, University of Granada, Granada, Spain
ITISE-2025 (11th International conference on Time Series and Forecasting) seeks to provide a forum for scientists, engineers, educators, and students to discuss the latest ideas and realizations in the foundations, theory, and models of and applicati...
The ITISE 2025 (11th International conference on Time Series and Forecasting) seeks to provide a discussion forum for scientists, engineers, educators and students about the latest ideas and realizations in the foundations, theory, models and applica...
A wavelet-based noise reduction method for time series is proposed. Traditional denoising techniques often adopt a “trial-and-error” approach, which can prove inefficient and may result in suboptimal filtering outcomes. In contrast, our m...
Short-term electrical demand forecasting is crucial for the efficient operation of modern power grids. Traditional methods often fail by neglecting system nonlinearities and external factors that influence electricity consumption. In this study, we p...
Dock door congestion is an essential and persistent concern within the realm of outbound warehouse logistics. The inability to accommodate outbound vehicles at the loading docks, especially during peak hours, disrupts internal warehouse operations, l...
Recent advances in Machine Learning have significantly improved anomaly detection in industrial screw driving operations. However, most existing approaches focus on binary classification of normal versus anomalous operations or employ unsupervised me...
Understanding human behavior is crucial for accurately predicting Electricity Load Demand (ELD), as daily habits and routines directly influence electricity consumption patterns across temporal and spatial domains. Two approaches for representing hum...
We present a dynamical systems approach to modeling nonlinear flood dynamics using 20 years of water level data from Durazno, Uruguay. Flood events are identified, and their periodicity and temporal distribution are analyzed in relation to rain gauge...
Artificial neural networks (ANNs) adjust to the underlying behavior in the dataset using a training rule or optimizer. The most popular first-and second-order optimizers, Adam (AD) and Levenberg–Marquardt (LM), were compared with the aim of pre...
A concept towards current measurement in low and medium voltage power distribution networks is presented. The concentric magnetic field around the current-carrying conductor should be measured using a nitrogen-vacancy quantum magnetic field sensor. A...
A novel model of the electrical demand of a power system capable of representing the hourly power load and its dependence on temperature is presented. The application of the model to the Colombian system is described with an evaluation of the error o...
Nitrogen vacancy (NV) diamonds are promising room temperature quantum sensors. As the technology moves towards application, efficient use of energy and cost become critical for miniaturization. This work focuses on microwave-based spin control using...
Singular spectrum analysis (SSA) is a nonparametric method that can be applied to signal estimation. The extension of SSA to the complex-valued case, called CSSA, is considered. The accuracy of signal estimation using CSSA is investigated. An explici...
This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city an...
Intensive energy consumption in the building sector remains one of the primary contributors to climate change and global warming. Within Renewable Energy Communities (RECs), improving energy management is essential for promoting sustainability and re...
This paper aims to implement a fuzzy system for the purpose of forecasting the output of photovoltaic (PV) systems. A bibliometric review was conducted to establish a baseline, involving the exploration of six different configuration of fuzzy systems...
In the automotive industry, the supply of service parts—such as bumpers, batteries, and aero parts—is required even after the end of vehicle production, as customers need them for maintenance and repairs. To earn customer confidence, manu...
Traditional security detection methods struggle to identify zero-day attacks in Industrial Control Systems (ICSs), particularly within critical infrastructures (CIs) integrated with the Industrial Internet of Things (IIoT). These attacks exploit unkn...
It is fundamental, yet challenging, to accurately predict water levels at hydrological stations located along the banks of an open channel river due to the complex interactions between different hydraulic structures. This paper presents a novel appli...
Most existing precipitation data fusion methods rely on reliable precipitation values, such as those observed from ground-based rain gauges, to correct the satellite precipitation estimates (SPEs) that often involve systematic biases. However, such r...
