The Swedish Transport Agency has for a long time collected data on a monthly basis for different variables that are used to make predictions, short projections as well as longer projections. They have used SAS for producing statistical models in air transport. The model with the largest value of coefficient of determination is the method that has been used for a long time. The Swedish Transport Agency felt it was time for an evaluation of their models and methods of how projections is estimated, they would also explore the possibilities to use different, completely new models for forecasting air travel. This Bachelor thesis examines how the Holt-Winters method does compare with SARIMA, error terms such as RMSE, MAPE, R2, AIC and BIC  will be compared between the methods.  The results which have been produced showing that there may be a risk that the Holt-Winters models adepts a bit too well in a few variables in which Holt-Winters method has been adapted. But overall the Holt-Winters method generates better forecasts . / <p>Avbryt / Spara utkast</p>

The aim of this study has been to compare the accuracy of prognosis tools and how well they do with regards to seasonal variation and known deviations in order to decide which method is more suitable for companies with the goal of optimizing their staffing. The study has used Systembolaget as a case to compare two methods of prognosis. Data has been retrieved from Systembolaget and includes the company's sales. The methods that have been compared are Holt-Winters method and regression analysis. This has been done by applying the methods in order to generate a forecast and then evaluating the result through MAPE. Application of these methods was done with Excel and SPSS. The study showed that, under the present circumstances, both methods on average gave equally accurate forecasts. Holt-Winter's method however, forecasted more accurately week by week, while the regression analysis projection on average yielded almost equal percentage error. The conclusion that was generated by the study was that Holt-Winter's method is preferable for companies with this type of sale if the purpose is to generate a weekly accurate forecast. On the other hand, the conclusion may vary if methods are optimized and more data is available. / Målet med denna undersökning har varit att jämföra noggrannheten hos prognostiseringsverktyg och hur bra dessa tar hänsyn till säsongsvariationer och kända avvikelser för att avgöra vilket verktyg som är lämpligast för företag med som vill optimera sin bemanning. Studien har använt Systembolaget som fall för att jämföra två metoder för prognostisering. Data har hämtats från Systembolaget och omfattar företagets försäljning. Metoderna som har jämförts har varit Holt-Winters metod och Regressionsanalys. Detta har utförts genom att tillämpa metoderna för att få ut en prognos och sedan utvärdera resultatet genom MAPE. Tillämpningen av dessa metoder skedde via Excel och SPSS. Studien visade att under de befintliga omständigheterna gav båda metoder i snitt lika noggranna prognoser. Holt-Winters metod prognostiserade dock noggrannare vecka för vecka, medan regressionsanalysens prognos i snitt gav nästan lika stort procentuellt fel. Slutsatsen som genererades genom studien var att Holt-Winters metod är att föredra för företag med denna typ av försäljning om syftet är att få en så noggrann prognos för varje vecka. Däremot kan slutsatsen variera om metoderna optimeras och mer data finns tillgänglig.

Submitted by Neusa Fagundes (neusa.fagundes@unioeste.br) on 2018-02-09T11:48:18Z No. of bitstreams: 2 Ketllin_Conceição2017.pdf: 1478973 bytes, checksum: a97631da6b15f46cfa48d39a4a2e9892 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-02-09T11:48:18Z (GMT). No. of bitstreams: 2 Ketllin_Conceição2017.pdf: 1478973 bytes, checksum: a97631da6b15f46cfa48d39a4a2e9892 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-06-12 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The unplanned development of large urban centers as well as the industrial one have hindered sustainable awareness regarding the correct water application. The unruly use of this resource has drawn the scientific community attention, thus, several scientific trials have been carried out in order to evaluate the use and quality of water. The use of indicators is one of the possibilities to evaluate water quality, which aims at analyzing physical-chemical and biological characteristics, and so allowing the evaluation of water body quality. In order to quickly evaluate the parameters involving water quality index (WQI), the process capability index was used. Thus, this trial aims at determining the water quality index as well as applying statistical quality control methodologies to evaluate the monitoring of water quality in rivers. Therefore, a database with physical-chemical and microbiological parameters from Passaúna and Piraquara rivers was applied to carry out this research development. Those rivers are in Araucária and Piraquara cities, respectively. The water quality index was determined by the time series, and subsequently data were submitted to statistical process control, with Shewhart individuals, EWMA and CUSUM, control charts as well as the development of the process quality index. Based on these data, a model of forecast was developed according to Holt-Winters Additive method. The WQI detected that the studied rivers remained under an averaged quality until the year of 2000, however, since that year, it was possible to observe a decreasing trend in water quality of the evaluated rivers. This trend was also identified by control charts. The graphs were also able to identify that waters of the rivers were not under statistical control, identifying some points that exceeded the used control limits. The process capacity index showed that the Piraquara River was classified with better quality of water when compared to the Passaúna River. The Holt-Winters prediction method showed that both rivers would continue with a decreasing trend in WQI and consequently in water quality. The applied statistical tools proved to be fast and efficient to evaluate water quality control. Finally, it is suggested that other researches should be developed using the technique of statistical process control for environmental evaluation. / O desenvolvimento desordenado dos grandes centros urbanos e o desenvolvimento industrial vêm dificultando a conscientização sobre o uso racional e sustentável da água. O uso indiscriminado desse recurso vem chamando atenção da comunidade científica e percebe-se uma infinidade de trabalhos científicos a fim de que se avaliem o uso e a qualidade da água. Uma das formas de se avaliar a qualidade da água é pela utilização de indicadores, que têm por finalidade analisar as características físico-químicas e biológicas e, assim, permitir a avaliação da qualidade das águas de um corpo hídrico. Utilizou-se o índice de capacidade do processo para se avaliar de forma rápida os parâmetros que envolvem o índice de qualidade da água (IQA). Desse modo, objetivou-se com esse trabalho determinar o índice de qualidade da água bem como aplicar metodologias de controle de qualidade estatístico para avaliar o monitoramento de qualidade da água em rios. Portanto, para se realizar o desenvolvimento da pesquisa, foi utilizado um banco de dados com parâmetros físico-químicos e microbiológicos dos rios Passaúna e Piraquara, pertencentes às cidades de Araucária e Piraquara, respectivamente. O índice de qualidade da água foi determinado com a série temporal e, posteriormente, esses dados foram submetidos ao controle estatístico do processo, com os gráficos de controle de Shewhart individual, MMEP e CUSUM, além do desenvolvimento do índice de qualidade do processo. A partir desses dados foi desenvolvido um modelo de previsão com o método de Holt-Winters Aditivo. O IQA detectou que os rios mantiveram-se em qualidade média até o ano 2000, entretanto, a partir desse ano foi possível visualizar uma tendência decrescente na qualidade das águas dos rios avaliados. Essa tendência também foi identificada pelos gráficos de controle. Os gráficos foram capazes de identificar que as águas dos rios não estavam em controle estatístico, identificando alguns pontos que excederam os limites de controle utilizados. O índice de capacidade do processo demonstrou que o rio Piraquara foi classificado com águas de melhor qualidade, quando comparado ao rio Passaúna. O método de previsão de Holt-Winters demonstrou que continuaria com uma tendência decrescente no IQA e, consequentemente, na qualidade da água, em ambos os rios avaliados. As ferramentas estatísticas utilizadas demonstraram ser rápidas e eficientes para a avaliação do controle de qualidade de águas. Por fim, sugere-se que sejam desenvolvidas mais pesquisas utilizando a técnica do controle estatístico do processo para avaliação do meio ambiente.

[pt] Após o choque de racionamento de energia elétrica, decorrente do desequilíbrio entre oferta e demanda, os vários setores da sociedade brasileira constataram a real e iminente necessidade de diversificação das fontes de geração de energia elétrica e de seu uso racional. Busca-se hoje novas fontes, entre as quais a energia eólica, uma alternativa nova e promissora. A energia eólica está aumentando no mundo todo e o Brasil tem um enorme potencial devido a sua localização geográfica e o governo tem investido neste tipo de energia. O principal objetivo desta dissertação é estudar e desenvolver modelos de previsão de velocidade de vento, de curto prazo da velocidade do vento. Os métodos de amortecimento exponencial, em particular o método de Holt-Winters e suas variações, são apropriados para este contexto devido à sua alta adaptabilidade e robustez. Para aplicação da metodologia considerou-se o município de São João do Cariri (Estado de Paraíba), onde está localizada uma das estações de referência do projeto SONDA (Sistema Nacional de Dados Ambientais para o setor de energia). Será utilizado o método de Holt-Winters, que será comparado com os modelos: de persistência, neuro-fuzzy (ANFIS) e estatísticos. / [en] After the shock of electricity rationing, due to the imbalance between supply and demand, the various sectors of the Brazilian society found a real and imminent need to diversify sources of electricity generation and its rational use. New sources are searched today, including wind power, a promising new alternative. Wind energy has been increasing worldwide and Brazil has huge potential due to its geographical location and the government has invested in this type of energy. The main objective of this thesis is to study and develop forecasting models, of short-term wind speed. The methods of exponential smoothing, in particular the method Holt-Winters and its variations, are suitable in this context because of its high adaptability and robustness. The city of São João do Cariri (State of Paraíba), where it is located one of the reference stations of project SONDA (National Environmental Data for the energy sector) was chosen in order to apply the methodology. The method that will be used is Holt-Winters, who will be compared with the models: persistence, neuro-fuzzy (ANFIS) and statistics.

[pt] O ONS (operador nacional do sistema elétrico brasileiro) vem utilizando o software ANNSTLF produzido pelo EPRI/EUA (Eletrical Power Research Institute) para realizar a previsão do consumo de carga horária. Entretanto, as estimativas fornecidas pelo programa estão fundamentadas na metodologia de uma rede neural que, de certo modo, impede ao usuário de extrair uma maior interpretação dos resultados que são fornecidos pela rede. Assim sendo, este trabalho pesquisou os métodos univariados convencionais: Holt-Winters e Box e Jenkins, considerando suas formulações aperfeiçoadas e adaptadas às características próprias do tipo de série em questão. Isto é, assumindo a existência de dois ciclos sazonais: um diário e outro semanal. A vantagem destas técnicas univariadas, em comparação ao ANNSTLF, é principalmente a interpretabilidade das informações obtidas. Dessa forma, esta pesquisa permite também avaliar melhor o desempenho do ANNSTLF. / [en] The ONS (National Operator of the Brazilian electrical system) has been using the software ANNSTLF produced by EPRI/USA (Eletrical Power Research Institute) to carry out the forecast of the hourly load consumption. However, the estimates supplied by the program are based on the methodology of a neural net that, in a way, does not allow the user to extract a better interpretation of the results produced by the net. Therefore, investigates the conventional univaried methods: Holt-Winters and Box & Jenkins, considering its formulations perfected and adapted to the characteristics of the series understudy. That is, its assumed the existence of two seasonal cicles: daily and weekly. The advantage of these univariate techniques, in comparison to the ANNSTLF, is mainly the ability to interpret the model estimates. Also, this research also allows a better evaluation the performance of the ANNSTLF.

Made available in DSpace on 2014-07-29T15:08:23Z (GMT). No. of bitstreams: 1 dissertacaoPriscila.pdf: 645879 bytes, checksum: 1150784f73524c6b5341fd319cc9d608 (MD5) Previous issue date: 2009-01-02 / n the planning of the energetic operation, analysis and forecasts of the flow are very important. A huge difficulty in the forecast of flow is the seasonality presence, due to drought and flood periods in the year. Many scientists, with different methodologies, have been concerned with finding a best model, compared with the utilized by Brazil s system - Markovian Model. The Makovian Model, or selfregressive with order 1, is a Box & Jenkins methodology, and requires data handling to treat non-stationarity, or the use of regular models, requiring a hardly theoretical formulation for the statistical procedures. Therefore, the statistical models, autoregressive model with seasonality and Holt-Winters model, of treatment of temporal series are presented and, carried out the flow s analysis and forecast for three study groups, in two different (historical) horizons. The performance of the models was compared and the results showed that the proposed models presents better adjust than the model adopted by Brazilian system / No planejamento da operação energética, a análise e previsão de vazões são muito importantes. Uma grande dificuldade na previsão de vazões é a presença da sazonalidade, devido aos períodos de seca e cheia no ano. Muitos estudiosos, com metodologias diversas, têm se preocupado em encontrar um modelo de melhor ajuste, em comparação ao utilizado pelo sistema brasileiro, ou seja, o modelo auto-regressivo de ordem 1, que consiste numa metodologia de Box & Jenkins e exige manuseio nos dados para tratar a não-estacionariedade. O presente trabalho analisa e compara os modelo utilizados pelo sistema brasileiro (PAR), com modelo matemático que considera a sazonalidade dos dados (SAR) e o método de Holt-Winters e, modelos amplamente estudados como PARMA e ANFIS. O desempenho dos modelos foi comparado e os resultados mostraram que em muitos estudos os modelos PAR/PARMA e ANFIS apresentam melhor ajuste , no geral, em relação aos demais

[pt] Em um mundo globalizado, em contínua transformação, são cada vez mais freqüentes mudanças no perfil da demanda. Se não detectadas rapidamente, elas podem gerar impactos negativos no progresso de um negócio devido à baixa qualidade nas previsões de venda, que começam a gerar valores sistematicamente acima ou abaixo da demanda real indicando a presença de viés. Para evitar esse cenário, técnicas formais para detecção de viés podem ser incorporadas ao processo de previsão de demanda. Diante desse quadro, a presente dissertação compara os desempenhos, via simulação, das principais técnicas formais de detecção de viés em previsão de demanda presentes na literatura. Nesse sentido, seis técnicas são identificadas e analisadas. Quatro são baseadas em estatísticas Tracking Signal e duas são adaptadas de técnicas de Controle Estatístico de Processos. Os modelos de previsão de demanda monitorados pelas técnicas em questão são os de séries temporais estruturadas, associados ao método de amortecimento exponencial simples e ao método de Holt, respectivamente, para séries com nível médio constante e séries com tendência. Três tipos de alterações no perfil da demanda – que acarretam em viés na previsão – são examinados. O primeiro consiste em mudanças no nível médio em séries temporais de nível médio constante. O segundo tipo também considera séries temporais de nível médio constante, porém com o foco em surgimentos de tendências. O terceiro viés consiste em mudanças na tendência em series temporais com tendência pré-incorporada. Entre os resultados obtidos, destaca-se a conclusão de que, para a maioria das situações estudadas, as técnicas baseadas nas estatísticas Tracking Signal possuem desempenho superior às demais técnicas com relação à eficiência na detecção de viés. / [en] In a globalized world, in continuous transformation, changes in the demand pattern are increasingly frequent. If not rapidly detected, they can have a negative and persistent impact in the wellbeing of a business due to continuously poor quality sales forecasts, which begin to generate values systematically above or below the actual demand indicating the presence of bias. To avoid this happening, statistical techniques can be incorporated in a prediction process with the objective known as bias detection in demand forecasting. Considering this situation, the present dissertation compares, through simulation, the efficiency performance of the main existing formal techniques of monitoring demand forecasting models, with the view of bias detection. Six of such techniques are identified and analyzed in this work. Four are based on Tracking Signal Statistics and two are adapted from the Statistical Process Control approach. The demand forecasting models monitored by the techniques in question can be classified as structured time series, for a constant level or trend pattern, and using both the simple exponential smoothing and the Holt s methods. Three types of changes in the demand pattern - which result in biased prediction - are examined. The first one focus on simulated changes on the average level of various constant times series. The second type also considered various constant times series, but now simulating the appearance of different trends. And the third refers to simulate changes in trends in various times series with pre-established trends. Among the results attained, one stands out: the techniques based on Tracking Signal Statistics - when compares to other methods - showed superior performance insofar as efficient bias detection in demand forecasting.

This document focuses on Cisco Netflow technology and its possible usage in monitoring networks and detecting network anomalies. Based on the analysis of attacks at the network and transport layer is designed an application for selected security threats which detects its presence. The implementation section provides a system for predicting network traffic and related detecting deviations from the baseline on the basis of statistical data. Use of NetFlow technolgy is demonstrated on examples where the results of other current security and monitoring techniques have failed or did not provide sufficiently good results.

Purpose – Forecasting is a firm's ability to anticipate or predict the future demand givenon a set of assumptions. For a company to implement an appropriate forecast model whichcan make accurate assumptions, the model needs to be aligned with the company's businesssituation and enhanced through supply chain relationships. Therefore, the purpose of thisstudy is: Investigate how small sized wholesalers benefit from demand forecasting. The purpose is divided into two research questions RQ1: How can a company influenced by a seasonal demand select an appropriateforecast model according to its business environment? RQ2: Why do information sharing issues between supply chain partners occur and howcan wholesalers overcome this resistance? Method – The researchers executed a singular case study at one of the local small-sizedfurniture wholesalers in Sweden. The data collection methods implemented in this studyare interviews, document analysis and a survey addressed towards downstream membersof the wholesalers’ chain, retailers (five participants). The combination of both qualitativeas well as quantitative methods was based on a triangulation principle which helped theresearchers provide a comprehensive understanding of the problem as well as increasevalidity and credibility of the study. Findings – The result of the study raises the importance of selecting a forecast model inaccordance with the company's business situation. Furthermore, by the help of a selfdesigned four-step forecast process the company could identify its influencing factors(seasonality, lead-times, lack of information sharing, etc.), available data, and finally selectthe appropriate model corresponding with the business situation. In this study the Holt-Winters model was selected due to the promotion of simplicity considering the casecompany. Also, the issue regarding information sharing among supply chain partners wasidentified where retailers promotes the performance of the whole supply chain anddemands a partnership as a requirement for sharing information. Implications – As every firm is unique and different in its nature it therefore requires itsown specific forecast process in which can select the appropriate model. However, thestudy revealed how selecting the appropriate forecast model can enhance the businessmeeting their seasonal demand. Additionally, the fact that small-sized companies need toestablish a partnership to receive demand information from their retailers. Based on theresult, the study reveals how companies can enhance their situation through demandforecasting. Limitations - As each model is based on each specific company the results regarding theselected forecast model can be questioned. Furthermore, due to the limited time-period ofthe research a specific forecast process had to be constructed which could only cover thescope of the research and not how the forecast model performed over time. Therefore, alonger time-period of the research could have included extra activities in the forecastprocess which would have validated the model.

In today's world the amount of collected data increases every day, this is a trend which is likely to continue. At the same time the potential value of the data does also increase due to the constant development and improvement of hardware and software. However, in order to gain insights, make decisions or train accurate machine learning models we want to ensure that the data we collect is of good quality. There are many definitions of data quality, in this thesis we focus on the accuracy aspect. One method which can be used to ensure accurate data is to monitor for and alert on anomalies. In this thesis we therefore suggest a method which, based on historic values, is able to detect anomalies in time series as new values arrive. The method consists of two parts, forecasting the next value in the time series using Holt-Winters method and comparing the residual to an estimated Gaussian distribution. The suggested method is evaluated in two steps. First, we evaluate the forecast accuracy for Holt-Winters method using different input sizes. In the second step we evaluate the performance of the anomaly detector when using different methods to estimate the variance of the distribution of the residuals. The results indicate that the suggested method works well most of the time for detection of point anomalies in seasonal and trending time series data. The thesis also discusses some potential next steps which are likely to further improve the performance of this method. / I dagens värld ökar mängden insamlade data för varje dag som går, detta är en trend som sannolikt kommer att fortsätta. Samtidigt ökar även det potentiella värdet av denna data tack vare ständig utveckling och förbättring utav både hårdvara och mjukvara. För att utnyttja de stora mängder insamlade data till att skapa insikter, ta beslut eller träna noggranna maskininlärningsmodeller vill vi försäkra oss om att vår data är av god kvalité. Det finns många definitioner utav datakvalité, i denna rapport fokuserar vi på noggrannhetsaspekten. En metod som kan användas för att säkerställa att data är av god kvalité är att övervaka inkommande data och larma när anomalier påträffas. Vi föreslår därför i denna rapport en metod som, baserat på historiska data, kan detektera anomalier i tidsserier när nya värden anländer. Den föreslagna metoden består utav två delar, dels att förutsäga nästa värde i tidsserien genom Holt-Winters metod samt att jämföra residualen med en estimerad normalfördelning. Vi utvärderar den föreslagna metoden i två steg. Först utvärderas noggrannheten av de, utav Holt-Winters metod, förutsagda punkterna för olika storlekar på indata. I det andra steget utvärderas prestandan av anomalidetektorn när olika metoder för att estimera variansen av residualernas distribution används. Resultaten indikerar att den föreslagna metoden i de flesta fall fungerar bra för detektering utav punktanomalier i tidsserier med en trend- och säsongskomponent. I rapporten diskuteras även möjliga åtgärder vilka sannolikt skulle förbättra prestandan hos den föreslagna metoden.