Modelos escaláveis para previsão probabilística com séries temporais nebulosas

Submitted by Petronio Silva ([email protected]) on 2019-09-17T14:32:44Z No. of bitstreams: 2 license_rdf: 805 bytes, checksum: 00e5e6a57d5512d202d12cb48704dfd6 (MD5) Final_Thesis.pdf: 7930425 bytes, checksum: 698683d322773c4282afb9814c2b5089 (MD5)Approved for entry into archive by Roseli Oliveira ([email protected]) on 2019-09-17T18:41:24Z (GMT) No. of bitstreams: 2 license_rdf: 805 bytes, checksum: 00e5e6a57d5512d202d12cb48704dfd6 (MD5) Final_Thesis.pdf: 7930425 bytes, checksum: 698683d322773c4282afb9814c2b5089 (MD5)Approved for entry into archive by Roseli Oliveira ([email protected]) on 2019-09-17T19:20:06Z (GMT) No. of bitstreams: 2 license_rdf: 805 bytes, checksum: 00e5e6a57d5512d202d12cb48704dfd6 (MD5) Final_Thesis.pdf: 7930425 bytes, checksum: 698683d322773c4282afb9814c2b5089 (MD5)Made available in DSpace on 2019-09-17T19:20:06Z (GMT). No. of bitstreams: 2 license_rdf: 805 bytes, checksum: 00e5e6a57d5512d202d12cb48704dfd6 (MD5) Final_Thesis.pdf: 7930425 bytes, checksum: 698683d322773c4282afb9814c2b5089 (MD5) Previous issue date: 2019-09-02In the field of time series forecasting, the most known methods are based on pointforecasting. However, this kind of forecasting has a serious drawback: it does not quantifythe uncertainties inherent to natural and social processes neither other uncertaintiescaused by the data gathering and processing. Because this in last years the interval andprobabilistic forecasting methods have been gaining more attention of researches, speciallyon environmental and economical sciences. But these techniques also have their own issuesdue to the methods being black-boxes and requiring stochastic simulations and ensemblesof multiple forecasting methods which are computationally expensive.On the other hand, the data volume (number of instances) and dimensionality (numberof variables) have reached magnitudes even greater, due to the commoditizing of thecapturing and storing computational devices, in a phenomenon known as Big Data. Suchfactors impact directly on the model’s training and updating costs, and for time serieswith Big Data characteristics, the scalability became a decisive factor in the choosing ofpredictive methods.In this context the Fuzzy Time Series (FTS) methods emerge, which have been growing inrecent years due to their accurate results, easiness of implementation, low computationalcost and model explainability. The Fuzzy Time Series methods have been applied toforecast electric load, market assets, economical indicators, tourism demand etc. But thereis a lack on FTS literature regarding interval and probabilistic forecasting.This thesis proposes new scalable Fuzzy Time Series methods and discusses its applicationto point, interval and probabilistic forecasting of mono and multivariate time series, for oneto many steps ahead. The parameters and hyper-parameters are discussed and fine tunningalternatives are presented. Finally the proposed methods are compared with the mainFuzzy Time Series techniques and other literature approaches using environmental andstock market data. The proposed methods obtained promising results on point, intervaland probabilistic forecasting and presented low computational cost, making it useful for awide range of applications.No campo da previsão de séries temporais os métodos mais difundidos baseiam-se em predição por ponto. Esse tipo de previsão, no entanto, tem um sério inconveniente: ele não quantifica as incertezas inerentes aos processos naturais e sociais nem outras incertezas decorrentes da captura e processamento dos dados. Por isso nos últimos anos os métodos de previsão intervalar e probabilística têm ganhado a atenção dos pesquisadores, particularmente nas ciências climáticas e na econometria. Mas outro inconveniente vem do fato de grande parte dos métodos de previsão probabilística serem métodos de caixa preta e demandarem simulações estocásticas ou ensembles de métodos preditivos que são computacionalmente despendiosos. Por outro lado, o volume (número de registros) e a dimensionalidade (número de variáveis) dos dados vêm alcançando magnitudes cada vez maiores, graças ao barateamento dos dispositivos computacionais de captura e armazenamento de dados, um fenômeno conhecido como Big Data. Tais fatores impactam diretamente no custo de treinamento e atualização dos modelos e, para séries temporais com essas características, a escalabilidade tornou-se um fator decisivo na escolha dos métodos preditivos. Nesse contexto emergem os métodos de Séries Temporais Nebulosas, que vêm em crescente expansão nos últimos anos dado os seus resultados acurados, a facilidade de implementação dos métodos, o seu baixo custo computacional e a interpretabilidade de seus modelos. Os métodos de Séries Temporais Nebulosas têm sido utilizados em áreas como previsão de demanda energética, indicadores e ativos de mercado, turismo entre outras. Mas há lacunas na literatura de tais métodos referentes a escalabilidade para grandes volumes de dados e previsão probabilística e por intervalos. A presente tese propõe novos métodos escaláveis de Séries Temporais Nebulosas e investiga a aplicação desses modelos na previsão por ponto, intervalar e probabilística, para uma ou mais variáveis e para mais de um passo à frente. Os parâmetros e hiperparâmetros dos métodos são discutidos e são apresentadas alternativas de ajuste fino dos modelos. Os métodos propostos são então comparados com as principais técnicas de Séries Temporais Nebulosas e outros modelos estatísticos utilizando dados ambientais e do mercado de ações. Os modelos propostos apresentaram resultados promissores tanto nas previsões por ponto quanto nas previsões por intervalo e probabilísticas e com baixo custo computacional, tornando-os úteis para um vasta gama de aplicaçõe

petroniocandido/pyFTS

pyFTS: A Python library for Fuzzy Time Serie

Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series

We propose a combined method that is based on the fuzzy time series (FTS) and convolutional neural networks (CNN) for short-term load forecasting (STLF). Accordingly, in the proposed method, multivariate time series data which include hourly load data, hourly temperature time series and fuzzified version of load time series, was converted into multi-channel images to be fed to a proposed deep learning CNN model with proper architecture. By using images which have been created from the sequenced values of multivariate time series, the proposed CNN model could determine and extract related important parameters, in an implicit and automatic way, without any need for human interaction and expert knowledge, and all by itself. By following this strategy, it was shown how employing the proposed method is easier than some traditional STLF models. Therefore it could be seen as one of the big difference between the proposed method and some state-of-the-art methodologies of STLF. Moreover, using fuzzy logic had great contribution to control over-fitting by expressing one dimension of time series by a fuzzy space, in a spectrum, and a shadow instead of presenting it with exact numbers. Various experiments on test data-sets support the efficiency of the proposed method