Early Information on Active Cases in Zero Rejection Efforts for COVID-19 Patients in West Java Province 2021 Using the Feedforwards Multilayer Perceptron Neural Network

West Java noted, as of August 14, 2021, 653,741 people were confirmed positive for COVID-19. On the same date, the number of active COVID-19 cases in West Java was 65,000. There is a significant increase in active cases of COVID-19 in 2021 in West Java. In the period 5 June – 17 July 2021, there was an increase in the number of active cases by 95,532. In that period, active cases increased by 484%, and the Bed Occupancy Ratio (BOR) in West Java had jumped in June 2021 with the highest number of 91.6%, this figure far exceeded the WHO recommendation of 60% before finally continuing to decline and finally in August was at 30.69%. This has an impact on the incidence of patient rejection at the COVID-19 referral hospital. Active cases talk about COVID-19 patients who need medical treatment and new cases talk about the rate of spread of COVID-19 in West Java, so these two things are very strategic to study. In this study, active cases and new case were predicted using Multilayer Perceptron (MLP). The data used in this study were sourced from the COVID-19 Task Force. The data is the number of positive cases, recovered and died of COVID-19 sufferers in 34 provinces in Indonesia in the period 2 March 2020 - 14 August 2021. The results of the study found, from the results of the evaluation using data testing the number of active cases in the last 19 weeks, namely April 10 – August 14, 2021, MLP is accurate in predicting the number of active cases for the first coming week 17 times, and the next two weeks for the second week 12 times with an absolute percentage error (APE) < 20%. As for weekly new cases, MLP has been accurate 10 times for the next one week and 9 times for the next two weeks. It is hoped that the results of this study can be useful for the government as a reference in conditioning the hospital bed capacity to deal with active cases of COVID-19 in West Java in the next two weeks so that no COVID-19 patients are rejected by the hospital because the hospital is full

Co-movement clustering: A novel approach for predicting inflation in the food and beverage industry

In the realm of food and beverage businesses, inflation poses a significant hurdle as it affects pricing, profitability, and consumer’s purchasing power, setting it apart from other industries. This study proposes a novel approach; co-movement clustering, to predict which items will be inflated together according to historical time-series data. Experiments were conducted to evaluate the proposed approach based on real-world data obtained from the UK Office for National Statistics. The predicted results of the proposed approach were compared against four classical methods (correlation, Euclidean distance, Cosine Similarity, and DTW). According to our experimental results, the accuracy of the proposed approach outperforms the above-mentioned classical methods. Moreover, the accuracy of the proposed approach is higher when an additional filter is applied. Our approach aids hospitality operators in accurately predicting food and beverage inflation, enabling the development of effective strategies to navigate the current challenging business environment in hospitality management. The lack of previous work has explored how time series clustering can be applied to support inflation prediction. This study opens a new research paradigm to the related field and this study can serve as a useful reference for future research in this emerging area. In addition, this study work contributes to the data analytics research stream in hospitality management literature

Improving the Effect of Electric Vehicle Charging on Imbalance Index in the Unbalanced Distribution Network Using Demand Response Considering Data Mining Techniques

With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important optimally. One of the critical parameters in the electrical system management is the distribution network imbalance. There are several ways to improve and control network imbalances. One of these ways is to detect the behavior of bus imbalance profiles in the network using data analysis. In the past, data analysis was performed for large environments such as states and countries. However, after the emergence of smart grids, behavioral study and recognition of these patterns in small-scale environments has found a fundamental and essential role in the deep management of these networks. One of the appropriate methods in identifying behavioral patterns is data mining. This paper uses the concepts of hierarchical and k-means clustering methods to identify the behavioral pattern of the imbalance index in an unbalanced distribution network. For this purpose, first, in an unbalanced network without the electric vehicle parking, the imbalance profile for all busses is estimated. Then, by applying the penetration coefficient of 25 and 75 for electric vehicles in the network, charging/discharging effects on the imbalance profile is determined. Then, by determining the target cluster and using demand response, the imbalance index is improved. This method reduces the number of busses competing in demand response programs. Next, using the concept of classification, a decision tree is constructed to minimize metering time

Features Extraction from Time Series

Time series can be found in various domains like medicine, engineering, and finance. Generally speaking, a time series is a sequence of data that represents recorded values of a phenomenon over time. This thesis studies time series mining, including transformation and distance measure, anomaly or anomalies detection, clustering and remaining useful life estimation. In the course of the first mining task (transformation and distance measure), in order to increase the accuracy of distance measure between transformed series (symbolic series), we introduce a novel calculation of distance between symbols. By integrating this newly defined method to symbolic aggregate approximation and its extensions, the experimental results show this proposed method is promising. During the process of the second mining task (anomaly or anomalies detection), for the purpose of improving the accuracy of anomaly or anomalies detection, we propose a distance measure method and an anomalies detection calculation. These proposed methods, together with previous published anomaly detection methods, are applied to real ECG data selected from MIT-BIH database. The experimental results show that our proposed outperforms other methods. During the course of the third mining task (clustering), we present an automatic clustering method, called AT-means, which can automatically carry out clustering for a given time series dataset: from the calculation of global average time series to the setting of initial centres and the determination of the number of clusters. The performance of the proposed method was tested on 10 benchmark time series datasets obtained from UCR database. For comparison, the K-means method with three different conditions are also applied to the same datasets. The experimental results show the proposed method outperforms the compared K-means approaches. During the process of the fourth mining task (remaining useful life estimation), all the original data are transformed into low-dimensional space through principal components analysis. We then proposed a novel multidimensional time series distance measure method, called as multivariate time series warping distance (MTWD), for remaining useful life estimation. This whole process is tested on the CMAPSS (Commercial Modular Aero Propulsion System Simulation) datasets and the performance is compared with two existing methods. The experimental results show that the estimated remaining useful life (RUL) values are closer to real RUL values when compared with the comparison methods. Our work contributes to the time series mining by introducing novel approaches to distance measure, anomalies detection, clustering and RUL estimation. We furthermore apply our proposed methods and related methods to benchmark datasets. The experimental results show that our methods are better than previously published methods in terms of accuracy and efficiency

Geospatial capacity allocation framework of wind and solar renewable generation for optimal grid support

Thesis (PhD)--Stellenbosch University, 2022.ENGLISH ABSTRACT: South Africa has displayed a unique energy supply profile over recent years, where the ability to consistently meet the energy demand has been constrained by physical limitations of the current energy supply infrastructure. The inadequate supply infrastructure results in countrywide loadshedding events, where total energy supply within high demand periods cannot be met. Low-grade coal, poorly maintained power plants and the impending decommissioning of existing thermal plants adds to the country’s energy supply deficit. Inadequate supply in high demand periods typically requires response from expensive on demand dispatch units, which are often non-renewable resources. This also equates to a decrease in grid supply stability. It is expected that optimised geospatial capacity allocation of new build wind and solar plants can assist in addressing the generation capacity constraints in the medium to longer term future. The framework proposed in this study favours a cascaded optimisation strategy, whereby the residual load profile is optimised statistically to reduce the requirements of ancillary services to complement baseload generation. In support of a reliable future energy supply scenario with high penetration of renewable energy, the optimisation framework proposed in this work represents a probabilistic risk-based approach that seeks to minimise the number of events where high residual load values require ancillary service interventions to maintain power balance. In this approach, renewable energy resource features are categorised in terms of the statistical properties of the spatiotemporal wind and solar power profiles for a given set of daily and seasonal Time-of-Use periods. In this context, it is recognised that the resource characteristics and grid impact of wind and solar generation profiles can be interpreted with reference to the daily and seasonal cycles exhibited by the demand profiles, wherein some Time-of-Use periods are more important than others. Apart from the benefit of assigning renewable energy capacities to spatial regions rather than specific coordinates, clustering reduces the dimensions of input data sets dramatically. This reduces the dimensionality of the multi-variable optimisation search space, which translates to reduced risk of local minima and reduced computational cost. The proposed framework has been implemented for a number of baseline case studies and optimisation case studies. It is concluded that the framework is highly flexible in the sense that the formulation of the minimum and maximum allocation constraints allow application for real-world scenarios where capacity allocation constraints apply on a regional level. Overall, the optimisation framework provides a robust method for the geospatial capacity allocation of wind and solar resources. The framework employs a robust way of handling constraint scenarios when considering multiple highly granular resource clusters.AFRIKAANSE OPSOMMING: Suid-Afrika het die afgelope jare 'n unieke energie-voorsienings-profiel getoon, waar die vermoë om konsekwent aan die energievraag te voldoen deur fisiese beperkings van die huidige energievoorsienings-infrastruktuur. Die onvoldoende voorsienings-infrastruktuur lei tot landswye beurtkraggebeurtenisse, waar die totale energie voorraad tydens hoë aanvraag periodes nie nagekom kan word nie. Laegraadse steenkool, swak onderhoud op kragsentrales en die naderende afskakel van bestaande termiese aanlegte dra by tot die land se tekort aan energie-voorsiening. ʼn Onvoldoende aanbod tydens hoë-aanvraag-periodes vereis tipies ʼn onmiddellike reaksie vanaf die kragopwekker, waar duurder intydse elektrisiteits-eenhede opgewek moet word. Hierdie eenhede is gewoonlik afkomstig vanaf niehernubare hulpbronne en plaas addisionele druk op krag-stelsel-stabiliteit. Daar word verwag dat die beperkings op opwekkings-kapasiteit, in die medium- tot langtermyn toekoms, aangespreek kan word deur die geoptimaliseerde georuimtelike-kapasiteits-toewysing van nuwe winden sonkrag-aanlegte. Die raamwerk wat in hierdie studie voorgestel word, bevoordeel 'n kaskadeoptimeringstrategie, waardeur die oorblywende-lasprofiel statisties geoptimaliseer word om die vereistes van bykomende dienste te verminder om basislading-opwekking aan te vul. Ter ondersteuning van 'n betroubare toekomstige energie-voorsienings-scenario met 'n hoë penetrasie van hernubare energie, verteenwoordig die voorgestelde optimaliserings-raamwerk 'n risiko-gebaseerde waarskynlikheids-benadering wat poog om die aantal gebeurtenisse te minimaliseer waar hoë oorblywende laswaardes aanvullende diens-ingryping vereis om die kragbalans te handhaaf. In hierdie benadering word hernubare-energie-hulpbron-kenmerke gekategoriseer. Dit word gedoen volgens die statistiese eienskappe van die tydruimtelike wind- en sonkragprofiele, vir 'n gegewe stel daaglikse en seisoenale tyd-van-gebruik periodes. In hierdie konteks word erken dat die hulpbron-kenmerke van winden sonkragkragstelsels se opwekkings-profiele geïnterpreteer kan word met verwysing na die daaglikse en seisoenale siklusse, soos vertoon deur die aanvraag-profiel. In hierdie aanvraag-profiel is daar ook sommige tyd-van-gebruik periodes wat belangriker is as ander. Afgesien van die voordeel om hernubare energie-vermoëns aan ruimtelike streke toe te ken, eerder as spesifieke koördinate, verminder die groepering van die insetdatastel-afmetings dramaties. Dit verminder die dimensionaliteit van die multiveranderlike optimaliserings-soekruimte, wat neerkom op ʼn verminderde risiko van plaaslike minima en berekenings-koste. Die voorgestelde raamwerk is geïmplementeer vir 'n aantal basislyn-scenarios en optimaliseringsgevallestudies. Daar word tot die gevolgtrekking gekom dat die raamwerk hoogs buigsaam is rakende die formulering van die minimum en maksimum toekennings-beperkings-toepassing, soos toegelaat vir werklike scenarios waar kapasiteits-toekennings-beperkings op 'n streeksvlak geld. In die algemeen bied die optimaliseringsraamwerk 'n robuuste metode vir die georuimtelike-kapasiteits toewysing van wind- en sonkragbronne. Die raamwerk gebruik 'n robuuste manier om beperkingscenarios te hanteer wanneer verskeie hoogs korrelvormige hulpbrongroeperings oorweeg word.Doctora

Modelagem simbólica de padrões morfológicos para classificação de séries temporais

Orientador : Prof. Dr. Fabiano SilvaTese (Doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 14/09/2015Inclui referências : f. 149-167Resumo: O contínuo armazenamento de dados ao longo do tempo, tais como séries temporais, tem motivado o desenvolvimento de novas abordagens baseadas em métodos de mineração de dados. Nesse cenário, uma nova área de pesquisa emergiu durante as últimas duas décadas, a mineração de dados em séries temporais. Mais especificamente, as abordagens baseadas em técnicas de aprendizado de máquina têm apresentado maior interesse entre os pesquisadores. Dentre as tarefas de mineração de dados, a classificação de séries temporais tem sido amplamente explorada, de modo que estudos recentes, utilizando algoritmos de aprendizado não simbólicos, têm reportado resultados significativos, em termos da acurácia de classificação. No entanto, em aplicações que envolvem processos de auxílio à tomada de decisão, tais como diagnóstico médico, controle de produção industrial, sistemas de monitoração de segurança em aeronaves ou usinas de energia elétrica, é necessário possibilitar o entendimento do raciocínio utilizado no processo de classificação. A primitiva shapelet foi proposta na literatura como um descritor de características morfológicas locais para possibilitar melhor compreensão dos conceitos, devido a sua maior proximidade com a percepção humana na identificação de padrões em séries temporais. Contudo, a maioria dos trabalhos relacionados ao estudo dessa primitiva tem se dedicado ao desenvolvimento de abordagens mais eficientes em termos de tempo e de acurácia, desconsiderando a necessidade da inteligibilidade dos classificadores. Nesse contexto, neste trabalho foi proposto um método que utiliza a transformada shapelet para a construção de modelos simbólicos de classificação por meio de uma abordagem híbrida que combina a representação de árvore de decisão com o algoritmo vizinho mais próximo. Também, foram desenvolvidas estratégias para melhorar a qualidade de representação da transformada shapelet na utilização de classificadores simbólicos, como árvores de decisão. Para avaliar o desempenho dessas propostas, foi conduzida uma avaliação experimental que envolveu a comparação com os algoritmos considerados estado da arte usando conjuntos de dados amplamente estudados na literatura de classificação de séries temporais. Com base nos resultados e análises realizadas nesta tese, foi possível verificar que a melhoria do processo de identificação de shapelets possibilita a construção de classificadores inteligíveis e competitivos; e que métodos híbridos podem contribuir para prover uma representação simbólica dos modelos, com desempenho equivalente ou até mesmo superior aos métodos não simbólicos. Palavras-chave: mineração de dados. aprendizado de máquina. séries temporais. classificação. modelos simbólicos.Abstract: The large amount of stored data over time, such as time series, has motivated the development of new approaches based on data mining methods. In this context, a new research area has emerged over the last two decades, the time series data mining. In particular, the approaches based on machine learning techniques have shown large interest among researchers. Among the data mining tasks, the time series classification has been widely exploited. Recent studies using non-symbolic learning algorithms have reported significant results in terms of classification accuracy. However, in applications related to decision making process, such as medical diagnosis, industrial production control, security monitoring systems in aircraft and in power plants, it is necessary allow the understanding of the reasoning used in the classification process. To take this into account, the shapelet primitive has been proposed in the literature as a descriptor of local morphological characteristics, which is closer to human perception for patterns identification in time series. On the other hand, most of the existing work related to shapelets has been dedicated to the development of more effective approaches in terms of time and accuracy, disregarding the need for interpretability of the classifiers. In this work, we propose to build symbolic models for time series classification using the shapelet transformation. This method is based on a hybrid approach that merges the decision tree representation and the nearest neighbor algorithm. Also, we developed strategies to improve the representation quality of the shapelet transformation using feature selection algorithms. We performed an experimental evaluation to analyze the performance of our proposals in comparison to the algorithms considered state of the art using datasets widely studied in the literature of time series classification. Based on the results and analysis carried out in this thesis, we found that the improvement of shapelet representation allows the construction of interpretable and competitive classifiers. Moreover, we found that the hybrid methods can help to provide symbolic models with equivalent or even superior performance to non-symbolic methods. Keywords: data mining. machine learning. time series. classification. symbolic models

The 8th International Conference on Time Series and Forecasting

The aim of ITISE 2022 is to create a friendly environment that could lead to the establishment or strengthening of scientific collaborations and exchanges among attendees. Therefore, ITISE 2022 is soliciting high-quality original research papers (including significant works-in-progress) on any aspect time series analysis and forecasting, in order to motivating the generation and use of new knowledge, computational techniques and methods on forecasting in a wide range of fields