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

Machine Learning for Financial Prediction Under Regime Change Using Technical Analysis: A Systematic Review

Recent crises, recessions and bubbles have stressed the non-stationary nature and the presence of drastic structural changes in the financial domain. The most recent literature suggests the use of conventional machine learning and statistical approaches in this context. Unfortunately, several of these techniques are unable or slow to adapt to changes in the price-generation process. This study aims to survey the relevant literature on Machine Learning for financial prediction under regime change employing a systematic approach. It reviews key papers with a special emphasis on technical analysis. The study discusses the growing number of contributions that are bridging the gap between two separate communities, one focused on data stream learning and the other on economic research. However, it also makes apparent that we are still in an early stage. The range of machine learning algorithms that have been tested in this domain is very wide, but the results of the study do not suggest that currently there is a specific technique that is clearly dominant

Evaluating efficient market hypothesis with stock clustering

This study investigates the validity of Efficient Market Hypothesis (EMH) by taking clusters of firms, generated using Self-Organising Maps (SOMs), and comparing their financial performance. Clusters were generated using 10 different financial variables as inputs to SOMs of different sizes. The effectiveness of the clustering was analysed using Silhouette Width, Davies-Bouldin Index and two Dunn’s Index metrics. The financial performance of the clusters was investigated using equal and value weighted returns and portfolio standard deviation. Market capitalisation was the only variable able to generate statistically significant results – in particular larger firms outperformed their smaller counterparts. It was concluded that this difference could be attributed to the volatile time frame chosen (2007-2012) which resulted in investors favouring larger firms. For future work it is recommended that researchers focus more on pre-processing the inputs, using different clusterin

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

Financial forecasting through unsupervised clustering and neural networks

In this paper, we review our work on a time series forecasting methodology based on the combination of unsupervised clustering and artificial neural networks. To address noise and non-stationarity, a common approach is to combine a method for the partitioning of the input space into a number of subspaces with a local approximation scheme for each subspace. Unsupervised clustering algorithms have the desirable property of deciding on the number of partitions required to accurately segment the input space during the clustering process, thus relieving the user from making this ad hoc choice. Artificial neural networks, on the other hand, are powerful computational models that have proved their capabilities on numerous hard real-world problems. The time series that we consider are all daily spot foreign exchange rates of major currencies. The experimental results reported suggest that predictability varies across different regions of the input space, irrespective of clustering algorithm. In all cases, there are regions that are associated with a particularly high forecasting performance. Evaluating the performance of the proposed methodology with respect to its profit generating capability indicates that it compares favorably with that of two other established approaches. Moving from the task of one-step-ahead to multiple-step-ahead prediction, performance deteriorates rapidly