ITISE 2026 - 24 articles
Canaria, Spain | 15–17 July 2026
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
In this paper, we develop the Poisson Integer-Valued Autoregressive (INAR) and negative binomial (NB) INAR models for longitudinal count data. As for the estimation of model parameters, we use the generalized estimating equation (GEE), generalized qu...
High-frequency time-series data applications generate data volumes that quickly exceed the strict memory and computational limits of ultra-low-power microcontrollers, making end-to-end deep learning approaches impractical for embedded machine learnin...
Tourism is one of the fastest-growing sectors of the global economy and plays a crucial role in economic development, employment generation, and regional competitiveness. In recent years, Georgia has experienced a notable increase in international to...
Hyperparameter optimisation (HPO) pipelines for demand forecasting almost always minimise the magnitude of the error (MSE, MAPE, etc.), a metric that measures how large errors are but says nothing about their direction. A forecast can score well on M...
Economic systems are complex, adaptive and interact with other systems. Their characteristics include stochastic and non-stationary behavior, complex interrelationships between elements, and small data samples. This makes it difficult to monitor and...
Housing tenure status is a core determinant of both social sustainability and urban quality of life. This paper examines changes in urban housing tenure status in Iran between 1974 and 2023, as well as forecasts future trends from 2024 to 2033 based...
This study benchmarks 16 online change point detection algorithms on both synthetic and real-world time series. The synthetic benchmark evaluates performance under controlled combinations of noise level, change magnitude, and change type, whereas the...
The sustained growth of urban populations has worsened chronic traffic congestion, with detrimental impacts on multiple dimensions of urban livability, including increased commute times, reduced road safety, and the degradation of local air quality....
This paper examines artificial intelligence (AI) adoption as a dynamic, temporally unfolding organizational process and develops a qualitative foundation for forecasting-oriented research on enterprise AI scaling. Drawing on 30 in-depth semi-structur...
Forecasting fuel injector core returns is a critical planning activity for heavy-duty diesel remanufacturers, yet industry practice still relies on univariate methods that cannot absorb external leading indicators. This paper proposes the first covar...
In developing countries, the informal economy represents 30 to 60 percent of gross domestic product and employs more than half of the workforce, yet its role in moderating the benefits of international trade remains underexplored. This study investig...
Performing imputation of gaps in irradiance time series plays a fundamental role in applications related to environmental monitoring and photovoltaic (PV) systems. Consequently, numerous studies have explored the use of artificial intelligence techni...
Forecasting chaotic and nonlinear time series in financial and economic domains remains an open challenge, as conventional statistical and machine learning methods struggle to capture their complex dynamics. We propose ESN-F, an Echo State Network en...
Ensuring the availability of didactic materials within Brazil’s National Textbook Program (PNLD) depends on reliable school enrollment forecasts. However, administrative time series often exhibit limited historical coverage and heterogeneous stabilit...
Heart variability series exhibit complex behavior best understood through nonlinear dynamical techniques. This research evaluates the utility of three Hurst exponent estimators for assessing persistence and antipersistence in RR time series from heal...
Logistic regression is widely used for binary risk prediction, but complete or quasi-separation can make maximum-likelihood fitting unstable and drive coefficients and fitted probabilities to extreme values. We present a stabilization idea based on s...
This article develops an asymptotic theory for the sample (time-averaged) variance of the locally stationary wavelet packet (LSWP) processes. Our focus is the periodogram and the sample variance, and in particular its average. Under a near-epoch-depe...
This paper proposes a dynamic profit maximization model for a monopolistic provider of AI infrastructure under conditions of market saturation and technological depreciation of infrastructure. The model is formulated within an optimal control framewo...
In many signal processing problems, such as GPS navigation, target tracking, and speech enhancement, the available data can be modeled a priori within a state-space framework. This framework describes how the state vector evolves over time and how it...
This study investigates the effect of the minimum wage (MW) on industrial employment (IndEmp) in Brazil, using the Kaitz Index (minimum wage/median wage). Analyzing monthly data from 2002 to 2016 with an Autoregressive Distributed Lag (ARDL) model, t...
This study evaluates parameter estimation sensitivity in long-memory time series models, focusing on the Autoregressive Fractionally Integrated Moving Average (ARFIMA) and the Fractionally Integrated Generalized Autoregressive Conditional Heteroskeda...
Detrended fluctuation analysis (DFA) is a widely used method for estimating the Hurst exponent of time series exhibiting nonstationary trends. In its standard formulation, DFA relies on the least-squares (LS) criterion at several stages of the proced...
Energy consumption and cryptocurrency mining exert significant yet asymmetric pressures on global CO2 emissions, presenting complex challenges for climate policy design. While prior research has examined fossil fuel and digital asset markets in isola...
Time series analysis faces fundamental challenges when series contain trends, structural breaks, volatility, or anomalies that violate stationarity assumptions. Existing statistical tests produce inconsistent results on complex real-world patterns, w...
