López Gonzales, Javier Linkolk

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López Gonzales, Javier Linkolk
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linkolklg@upeu.edu.pe
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  • Publicacion
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    Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan
    (2023-03-09)
    Hasnain Iftikhar
    ;
    Nadeela Bibi
    ;
    Paulo Canas Rodrigues
    ;
    Javier Linkolk López‐Gonzales  
    In today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive analysis of forecasting monthly electricity consumption by comparing several decomposition techniques followed by various time series models. To this end, first, we decompose the electricity consumption time series into three new subseries: the long-term trend series, the seasonal series, and the stochastic series, using the three different proposed decomposition methods. Second, to forecast each subseries with various popular time series models, all their possible combinations are considered. Finally, the forecast results of each subseries are summed up to obtain the final forecast results. The proposed modeling and forecasting framework is applied to data on Pakistan’s monthly electricity consumption from January 1990 to June 2020. The one-month-ahead out-of-sample forecast results (descriptive, statistical test, and graphical analysis) for the considered data suggest that the proposed methodology gives a highly accurate and efficient gain. It is also shown that the proposed decomposition methods outperform the benchmark ones and increase the performance of final model forecasts. In addition, the final forecasting models produce the lowest mean error, performing significantly better than those reported in the literature. Finally, we believe that the framework proposed for modeling and forecasting can also be used to solve other forecasting problems in the real world that have similar features.
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    Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange
    (2024-01-01)
    Hasnain Iftikhar
    ;
    Murad Khan
    ;
    Josué Edison Turpo Chaparro  
    ;
    Paulo Canas Rodrigues
    ;
    Javier Linkolk López‐Gonzales  
    <abstract><p>Traders and investors find predicting stock market values an intriguing subject to study in stock exchange markets. Accurate projections lead to high financial revenues and protect investors from market risks. This research proposes a unique filtering-combination approach to increase forecast accuracy. The first step is to filter the original series of stock market prices into two new series, consisting of a nonlinear trend series in the long run and a stochastic component of a series, using the Hodrick-Prescott filter. Next, all possible filtered combination models are considered to get the forecasts of each filtered series with linear and nonlinear time series forecasting models. Then, the forecast results of each filtered series are combined to extract the final forecasts. The proposed filtering-combination technique is applied to Pakistan's daily stock market price index data from January 2, 2013 to February 17, 2023. To assess the proposed forecasting methodology's performance in terms of model consistency, efficiency and accuracy, we analyze models in different data set ratios and calculate four mean errors, correlation coefficients and directional mean accuracy. Last, the authors recommend testing the proposed filtering-combination approach for additional complicated financial time series data in the future to achieve highly accurate, efficient and consistent forecasts.</p></abstract>
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    Modeling and forecasting carbon dioxide emission in Pakistan using a hybrid combination of regression and time series models
    (2024-06-19)
    Hasnain Iftikhar  
    ;
    Murad Khan
    ;
    Justyna Żywiołek
    ;
    Mehak Khan
    ;
    Javier Linkolk López‐Gonzales  
    Carbon dioxide (CO2) emissions continue to rise globally despite efforts to combat climate change. Energy industry emissions are a pressing global issue, causing devastating impacts. Hence, it is vital to accurately and efficiently forecast CO2 emissions. Thus, this study comprehensively analyzes forecasting CO2 emissions by comparing various hybrid combinations of regression and time series methods to explore the CO2 emissions in Pakistan. First, divide the yearly time series of CO2 emissions into the long-run curve trend series and the residual subseries. The long-run curve trend subseries is modeled using parametric and nonparametric regression methods, while various standard time series models are used to forecast the residual subseries. However, the forecasts of each subseries will be combined to obtain the final forecast of CO2 emissions. This work used four different accuracy mean errors, a statistical test, and a graphical analysis as performance measures to evaluate the proposed hybrid forecasting technique. The findings confirmed that the proposed hybrid combination forecasting technique is highly accurate and efficient in forecasting CO2 emissions. Likewise, according to the proposed final optimal hybrid combination forecasting model, Pakistan's per capita CO2 emissions will be 1.130215 metric tons in 2030. Pakistan's escalating emission trend signals that creative solutions must be implemented to curb it. Thus, the government must price carbon footprints, regulate electricity from zero-carbon sources, reduce population, encourage afforestation in densely populated areas, adopt clean technology, and fund research.
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    A Systematic Review of the Application of Maturity Models in Universities
    (2020-10-01)
    Esteban Tocto Cano  
    ;
    Sandro Paz Collado
    ;
    Javier Linkolk López‐Gonzales  
    ;
    Josué Edison Turpo Chaparro  
    A maturity model is a widely used tool in software engineering and has mostly been extended to domains such as education, health, energy, finance, government, and general use. It is valuable for evaluations and continuous improvement of business processes or certain aspects of organizations, as it represents a more organized and systematic way of doing business. In this paper, we only focus on college higher education. For this reason, we present a novel approach that allows detecting some gaps in the existing maturity models for universities, as they are not models that address the dimensions in their entirety. To identify these models and their validities, as well as a classification of models that were identified in universities, we carried out a systematic literature review on 27,289 articles retrieved with respect to maturity models and published in peer-reviewed journals between 2007 and 2020. We found 23 articles that find maturity models applied in universities, through exclusion and inclusion criteria. We then grouped these items into nine categories with specific purposes. We concluded that maturity models used in Universities move towards agility, which is supported by the semantic web.
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    An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations
    (2026-01-13)
    Hasnain Iftikhar
    ;
    Atef F. Hashem
    ;
    Liban Ali Mohamud
    ;
    A. S. Al-Moisheer
    ;
    Ronny Ivan Gonzales Medina
    Chronic Kidney Disease (CKD) remains a pressing global public health concern, accounting for approximately 1.7 million deaths annually and disproportionately affecting aging and underserved populations. The increasing burden of CKD, particularly in low-resource settings, underscores the urgent need for early, accurate, and scalable diagnostic tools. This study proposes a hybrid mathematical and artificial intelligence (AI) framework for the early prediction of CKD, with a focus on supporting healthcare strategies in aging and resource-limited communities. Utilizing clinical data from a case-control study conducted in District Buner, Khyber Pakhtunkhwa, Pakistan, the framework incorporates a structured modeling pipeline that involves data preprocessing (feature extraction, missing data imputation, and categorical encoding) and class balancing via the Synthetic Minority Over-Sampling Technique (SMOTE). The proposed system integrates multiple machine learning algorithms, including logistic regression, feedforward neural networks, decision trees, support vector machines, and random forests, within a novel ensemble learning strategy designed to enhance diagnostic precision. Model robustness was assessed using three distinct train–test scenarios: (90%, 10%), (75%, 25%), and (50%, 50%). Performance evaluation employed six metrics: accuracy, specificity, sensitivity, Youden index, Brier score, and F1 score, supported by comprehensive graphical and statistical analysis. The ensemble model consistently outperformed individual classifiers, achieving a mean accuracy of 97.71%, specificity of 97.19%, sensitivity of 99.84%, Youden index of 86.55, Brier score of 1.43%, and F1 score of 98.19%. Support vector machines and random forests ranked second and third, respectively, while decision trees exhibited the lowest performance. To the best of our knowledge, this is the first ensemble-based predictive framework for CKD developed using clinical data from Pakistan. The system holds strong potential for integration into real-world biomedical decision support systems, particularly in aging and underserved populations, thereby contributing to early detection, enhanced care delivery, and optimized resource utilization in the management of chronic diseases.
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    Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector
    (2022-01-14)
    Felipe Leite Coelho da Silva
    ;
    Kleyton da Costa
    ;
    Paulo Canas Rodrigues
    ;
    Rodrigo Salas
    ;
    Javier Linkolk López‐Gonzales  
    Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.
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    A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil
    (2024-06-29)
    Marlon Mesquita Lopes Cabreira
    ;
    Felipe Leite Coelho da Silva
    ;
    Josiane da Silva Cordeiro
    ;
    Ronald Miguel Serrano Hernández
    ;
    Paulo Canas Rodrigues
    The Brazilian industrial sector is the largest electricity consumer in the power system. Energy planning in this sector is important mainly due to its economic, social, and environmental impact. In this context, electricity consumption analysis and projections are highly relevant for the decision-making of the industrial sectorand organizations operating in the energy system. The electricity consumption data from the Brazilian industrial sector can be organized into a hierarchical structure composed of each geographic region (South, Southeast, Midwest, Northeast, and North) and their respective states. This work proposes a hybrid approach that incorporates the projections obtained by the exponential smoothing and Box–Jenkins models to obtain the hierarchical forecasting of electricity consumption in the Brazilian industrial sector. The proposed approach was compared with the bottom-up, top-down, and optimal combination approaches, which are widely used for time series hierarchical forecasting. The performance of the models was evaluated using the mean absolute percentage error (MAPE) and root mean squared error (RMSE) precision measures. The results indicate that the proposed hybrid approach can contribute to the projection and analysis of industrial sector electricity consumption in Brazil.
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    Random Forest Model for Optimizing Coagulant Doses in Drinking Water Treatment: Application at the Miguel de la Cuba Ibarra Plant
    (2025-12-30)
    Ronny Ivan Gonzales Medina
    ;
    Juan Adriel Carlos Mendoza
    ;
    Eduardo José Zuñiga Goyzueta
    ;
    Rosa María Morán-Silva
    ;
    Javier Linkolk López‐Gonzales  
    Optimizing coagulant dosages in Drinking Water Treatment Plants (DWTPs) is critical for reducing operational costs, minimizing chemical waste, mitigating environmental impacts, and ensuring consistent water quality, particularly in resource-constrained settings where conventional jar tests are labor-intensive and poorly suited to real-time demands. This study develops and validates a Random Forest (RF) machine learning model to predict optimal dosages of aluminum sulfate, polyaluminum chloride, and a polymer flocculant at the Miguel de la Cuba Ibarra DWTP in Peru, addressing the need for an efficient, real-time decision support system. Using a historical dataset of 2556 jar tests, a univariate RF model was developed to predict settled water turbidity, tailored to the plant’s typical operational range. The model demonstrated robust predictive performance, achieving a coefficient of determination (R2) of 0.92 during training and 0.76 during validation with unseen data, alongside a Root Mean Square Error (RMSE) of 0.11 NTU and a Mean Absolute Percentage Error (MAPE) of 0.11 in the training phase. Integrated into a digital platform, the model generates real-time NTU ppm dosing curves, providing a practical and responsive tool to enhance operational efficiency for DWTP operators. This work offers a scalable, data-driven solution to improve water treatment processes in resource-limited contexts.
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    Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives
    (2024-09-10)
    Hasnain Iftikhar
    ;
    Moiz Qureshi
    ;
    Justyna Żywiołek
    ;
    Javier Linkolk López‐Gonzales  
    ;
    Olayan Albalawi
    Particulate matter with a diameter of 2.5 microns or less ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> ) is a significant type of air pollution that affects human health due to its ability to persist in the atmosphere and penetrate the respiratory system. Accurate forecasting of particulate matter is crucial for the healthcare sector of any country. To achieve this, in the current work, a new time series ensemble approach is proposed based on various linear (autoregressive, simple exponential smoothing, autoregressive moving average, and theta) and nonlinear (nonparametric autoregressive and neural network autoregressive) models. Three ensemble models are also developed, each employing distinct weighting strategies: equal distribution of weight among all single models (ESME), weight assignment based on training average accuracy errors (ESMT), and weight assignment based on validation mean accuracy measures (ESMV). This technique was applied to daily <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> concentration data from 1 January 2019, to 31 May 2023, in Pakistan’s main cities, including Lahore, Karachi, Peshawar, and Islamabad, to forecast short-term <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m4"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> concentrations. When compared to other models, the best ensemble model (ESMV) demonstrated mean errors ranging from 3.60% to 25.79% in Islamabad, 0.81%–13.52% in Lahore, 1.08%–7.06% in Karachi, and 1.09%–12.11% in Peshawar. These results indicate that the proposed ensemble approach is more efficient and accurate for short-term <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m5"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> forecasting than existing models. Furthermore, using the best ensemble model, a forecast was made for the next 15 days (June 1 to 15 June 2023). The forecast showed that in Lahore, the highest <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m6"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> value (236.00 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m7"><mml:mi>μ</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> ) was observed on 8 June 2023. Other days also displayed higher and poor air quality throughout the 15 days. Conversely, Karachi experienced moderate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m8"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> concentration levels between 50 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m9"><mml:mi>μ</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> and 80 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m10"><mml:mi>μ</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> . In Peshawar, the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m11"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> concentration levels were consistently unhealthy, with the highest peak (153.00 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m12"><mml:mi>μ</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> ) observed on 9 June 2023. This forecasting experience can assist environmental monitoring organizations in implementing cost-effective planning to minimize air pollution.
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    Data-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning Approach
    (2026-05-05)
    Lucas Camaz Ferreira
    ;
    Felipe Leite Coelho da Silva
    ;
    Josiane da Silva Cordeiro
    ;
    Javier Linkolk López‐Gonzales  
    ;
    Esteban Tocto Cano  
    The Brazilian residential sector is one of the largest consumers of electricity, making residential energy consumption a critical component of national energy systems. Electricity consumption patterns in this sector are closely associated with household appliance ownership and, consequently, with socioeconomic status. For residential energy planning to operate more equitably and efficiently, it is essential that consumption analyses be aligned with the socioeconomic conditions of the population. This study examines the role of socioeconomic variables in residential energy planning through the application of supervised machine learning algorithms within a data-driven socioeconomic segmentation framework. Decision trees, support vector machines, and artificial neural networks were implemented using data from the Brazilian residential sector to evaluate model performance and to determine the extent to which household socioeconomic status can be inferred from variables related to appliance ownership and electricity consumption characteristics. The results showed that household appliances, such as refrigerators, microwave ovens, and air conditioners, exhibited substantial predictive power in relation to socioeconomic status, thus improving the interpretation and understanding of residential energy consumption from a multidimensional perspective. The neural network model achieved the highest predictive performance. By enabling data-driven socioeconomic segmentation based on observable electricity consumption patterns, this approach provides relevant insights for residential energy planning and contributes to more targeted and equitable energy policy design, supporting Sustainable Development Goal 7 on Affordable and Clean Energy and Sustainable Development Goal 10 on Reduced Inequalities.
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