The main goal of the diploma thesis is to analyze the development of commodity prices affecting the prices of materials and to prove their impact on the materials. The monitored materials were selected to represent the widest possible range of the construction market. The final result of the work is the prediction of the price of reference object for the next five years.

M. Tech. (Department of Information Technology, Faculty of Applied and Computer Sciences) Vaal University of Technology. / Cloud computing provides improved and simplified IT management and maintenance capabilities through central administration of resources. Companies of all shapes and sizes are adapting to this new technology. Although cloud computing is an attractive concept to the business community, it still has some challenges such as traffic management and traffic prediction that need to be addressed. Most cloud service providers experience traffic congestion. In the absence of effective tools for cloud computing traffic prediction, the allocation of resources to clients will be ineffective thus driving away cloud computing users. This research intends to mitigate the effect of traffic congestion on provision of cloud service by proposing a proactive traffic prediction model that would play an effective role in congestion control and estimation of accurate future resource demand. This will enhance the accuracy of traffic flow prediction in cloud computing by service providers. This research will evaluate to determine the performance between Auto-regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) as prediction tools for cloud computing traffic. These two techniques were tested by using simulation to predict traffic flow per month and per year. The dataset was downloaded data taken from CAIDA database. The two algorithms Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) where implemented and tested separately. Experimental results were generated and analyzed to test the effectiveness of the traffic prediction algorithms. Finally, the findings indicated that ARIMA can have 98 % accurate prediction results while ANN produced 89 % accurate prediction results. It was also observed that both models perform better on monthly data as compared to yearly data. This study recommends ARIMA algorithm for data flow prediction in private cloud computing

On a group level, satisfaction with botulinum neurotoxin (BoNT) treatment in neurological indications is high. However, it is well known that a relevant amount of patients may not respond as expected. The aim of this study is to evaluate the BoNT treatment outcome on an individual level using a statistical single-case analysis as an adjunct to traditional group statistics. The course of the daily perceived severity of symptoms across a BoNT cycle was analyzed in 20 cervical dystonia (CD) and 15 hemifacial spasm (HFS) patients. A parametric single-case autoregressive integrated moving average (ARIMA) time series analysis was used to detect individual responsiveness to BoNT treatment. Overall, both CD and HFS patients significantly responded to BoNT treatment with a gradual worsening of symptom intensities towards BoNT reinjection. However, only 8/20 CD patients (40%) and 5/15 HFS patients (33.3%) displayed the expected U-shaped curve of BoNT efficacy across a single treatment cycle. CD (but not HFS) patients who followed the expected outcome course had longer BoNT injection intervals, showed a better match to objective symptom assessments, and were characterized by a stronger certainty to control their somatic symptoms (i.e., internal medical locus of control). In addition to standard evaluation procedures, patients should be identified who do not follow the mean course-of-treatment effect. Thus, the ARIMA single-case time series analysis seems to be an appropriate addition to clinical treatment studies in order to detect individual courses of subjective symptom intensities.

Due to the growing share of renewable energy in countries' power systems, the need for precise forecasting of electricity consumption will increase. This paper considers two different approaches to time series forecasting, autoregressive moving average (ARMA) models and recurrent neural networks (RNNs). These are applied to Swedish electricity consumption data, with the aim of deriving simple yet efficient predictors. An additional aim is to analyse the impact of day of week and temperature on forecast accuracy. The models are evaluated on both long- and mid-term forecasting horizons, ranging from one day to one month. The results show that neural networks are superior for this task, although stochastic seasonal ARMA models also perform quite well. Including external variables only marginally improved the ARMA predictions, and had somewhat unclear effects on the RNN forecasting accuracy. Depending on the network model used, adding external variables had either a slightly positive or slightly negative impact on prediction accuracy.

During the COVID-19 pandemic, customer shopping habits have changed. Some industries experienced an abrupt shift during the pandemic outbreak while others navigate in new normal states. For some merchants, the highly-uncertain new phenomena of COVID-19 expresses as outliers in time series of volume of sales. As forecasting models tend to replicate past behavior of a series, outliers complicates the procedure of forecasting; the abnormal events tend to unreliably replicate in forecasts of the subsequent year(s). In this thesis, we investigate how to forecast volume of sales during the abnormal time period of COVID-19, where the classical ARIMA family of models produce unreliable forecasts. The research revolved around three time series exhibiting three types of outliers: a level shift, a transient change and an additive outlier. Upon detecting the time period of the abnormal behavior in each series, two experiments were carried out as attempts for increasing the predictive accuracy for the three extreme cases. The first experiment was related to imputing the abnormal data in the series and the second was related to using a combined model of a pre-pandemic and a post-abnormal forecast. The results of the experiments pointed at significant improvement of the mean absolute percentage error at significance level alpha=0.05 for the level shift when using a combined model compared to the pre-pandemic best-fit SARIMA model. Also, at significant improvement for the additive outlier when using a linear impute. For the transient change, the results pointed at no significant improvement in the predictive accuracy of the experimental models compared to the pre-pandemic best-fit SARIMA model. For the purpose of generalizing to large-scale conclusions of methods' superiority or feasibility for particular abnormal behaviors, empirical evaluations are required. The proposed experimental models were discussed in terms of reliability, validity and quality. By residual diagnostics, it was argued that the models were valid; however, that further improvements can be made. Also, it was argued that the models fulfilled desired attributes of simplicity, scaleability and flexibility. Due to the uncertain phenomena of the COVID-19 pandemic, it was suggested not to take the outputs as long-term reliable solutions. Rather, as temporary solutions requiring more frequent updating of forecasts. / Under coronapandemin har kundbeteenden och köpvanor förändrats. I vissa branscher upplevdes ett plötsligt skifte vid pandemiutbrottet och i andra navigerar handlare i nya normaltillstånd. För vissa handlare är förändringarna så pass distinkta att de yttrar sig som avvikelser i tidsserier över försäljningsvolym. Dessa avvikelser komplicerar prognosering. Då prognosmodeller tenderar att replikera tidsseriers tidigare beteenden, tenderas det avvikande beteendet att replikeras i försäljningsprognoser för nästkommande år. I detta examensarbete ämnar vi att undersöka tillvägagångssätt för att estimera försäljningsprognoser under den abnorma tidsperioden av COVID-19, då klassiska tidsseriemodeller felprognoserar. Detta arbete kretsade kring tre tidsserier som uttryckte tre avvikelsertyper: en nivåförskjutning, en övergående förändring och en additiv avvikelse. Efter att ha definierat en specifik tidsperiod relaterat till det abnorma beteendet i varje tidsserie, utfördes två experiment med syftet att öka den prediktiva noggrannheten för de tre extremfallen. Det första experimentet handlade om att ersätta den abnorma datan i varje serie och det andra experimentet handlade om att använda en kombinerad pronosmodell av två estimerade prognoser, en pre-pandemisk och en post-abnorm. Resultaten av experimenten pekade på signifikant förbättring av ett absolut procentuellt genomsnittsfel för nivåförskjutningen vid användande av den kombinerade modellen, i jämförelse med den pre-pandemiskt bäst passande SARIMA-modellen. Även, signifikant förbättring för den additiva avvikelsen vid ersättning av abnorm data till ett motsvarande linjärt polynom. För den övergående förändringen pekade resultaten inte på en signifikant förbättring vid användande av de experimentella modellerna. För att generalisera till storskaliga slutsatser giltiga för specifika avvikande beteenden krävs empirisk utvärdering. De föreslagna modellerna diskuterades utifrån tillförlitlighet, validitet och kvalitet. Modellerna uppfyllde önskvärda kvalitativa attribut såsom enkelhet, skalbarhet och flexibilitet. På grund av hög osäkerhet i den nuvarande abnorma tidsperioden av coronapandemin, föreslogs det att inte se prognoserna som långsiktigt pålitliga lösningar, utan snarare som tillfälliga tillvägagångssätt som regelbundet kräver om-prognosering.

In recent years more companies have invested in electronic commerce as a result of more customers using the internet as a tool for shopping. However, the basics of marketing still apply to online stores, and thus companies need to conduct market analyses of customers and the online market to be able to successfully target customers online. In this report, we propose the use of machine learning, a tool that has received a lot of attention and positive affirmation for the ability to tackle a range of problems, to predict future trends of electronic commerce in Sweden. More precise, to predict the future share of users of electronic commerce in general and for certain demographics. We will build three different models, polynomial regression, SVR and ARIMA. The findings from the constructed forecasts were that there are differences between different demographics of customers and between groups within a certain demographic. Furthermore, the result showed that the forecast was more accurate when modelling a certain demographic than the entire population. Companies can thereby possibly use the models to predict the behaviour of certain smaller segments of the market and use that in their marketing to attract these customers. / Pa senare år har många företag investerat i elektronisk handel, även kallat e-handel, vilket är ett resultat av att individer i samhället i större utsträckning använder internet som ett redskap. Grunderna för marknadsföring gäller fortfarande för webbaserade butiker, och därmed behöver företag genomföra marknadsanalyser över potentiella kunder och internet-marknaden för att kunna lansera starka marknadsföringskampanjer. I denna rapport föreslår vi användning av maskininlärning, ett verktyg som har fått mycket uppmärksamhet på senaste tiden för dess förmåga att hantera olika problem kring data och för att prognostisera framtida trender för e-handel i Sverige. Mer exakt kommer andelen användare av e-handel i framtiden prognostiseras, både generellt och för enskilda demografier. Vi kommer att implementera tre olika modeller, polynomisk regression, SVR och ARIMA. Resultaten från de konstruerade prognoserna visar att det finns tydliga skillnader mellan olika demografier av kunder och mellan grupper inom en viss demografi. Dessutom visade resultaten att prognoserna var mer exakta vid modellering av en viss demografi än över hela befolkningen. Företag kan därmed möjligtvis använda modellerna för att förutsäga beteendet hos vissa mindre segment av marknaden.

The study aimed to define and investigate relevant parameters affecting manual frequency restoration reserve (mFRR) volumes of the balancing market in the Finnish price area. It also aimed to find suitable models and investigate Day-ahead prediction possibilities of mFRR volumes. Parameters related to mFRR volumes Day-ahead predictions were identified in several earlier studies where of nine parameters were investigated. The correlations between mFRR volumes and the different parameters were investigated using Spearman’s correlation. Different linear machine learning models for Day-ahead predictions of mFRR volumes were builtand tested in Python. The resulting models used for predicting mFRR volumes in Python were one ARIMAX model and one SARIMAX model. The models were validated with a walk-forward method where Day-ahead predictions were conducted monthly for one year. The accuracy of the predictions was measured by the validation parameters Mean Absolute Value, Root Mean Square Error and Median Absolute Deviation. Results from the study show that it is difficult to predict absolute activated mFRR volumes. Although, it might be possible to predict that mFRR volumes will be activated or not, up- or down regulated to some extent. One explanation of the difficulties in predicting mFRR volumes is dueto mFRR being a balancing product whose function is to regulate disturbances in the electricity grid.

一. 研究動機與目的： 近年來國內股市蓬勃發展，尤其自民國76年起，股價指數與成交量大幅攀升；從最初 的800 至 900點與 100至 200億的成交規模，到近來屢破紀錄，曾達12，000 多點與 1，900多億高指數與高成交量的空前盛況；依目前情景，台灣股市之再創新猷應是指 日可待的。 這三年多來，國內股市也曾呈現空頭現象，但其間指數回檔、幅度之大與速度之快， 均非事先所能料及。亦即這數次回跌，並非總體經濟因素之基本分析所能解釋，亦非 技術指標分析所能捉摸。最明顯的是，這幾次股價下跌，均有理論上非影響股價之外 生因素摻雜其間，造成突發性的利空，促使股價回軟甚至鉅幅下跌。事實上，影響股 價之因素錯綜複雜；國內相關之研究與文獻極多；歸納言之，其特色為：雖均嚐試尋 找可解釋之原因及其間的函數關係，唯大多只考慮單解釋變數或僅探討經濟因素對股 價的影響。而影響台灣股市相當重要的非經濟因素，如制度和政策之施行與變革等突 發性外生變數對股市衝擊之研究卻付諸闕如。 本文之目的，卻在探討一突發事件；如證所稅，對特定現象，如台灣股價指數，干預 後所產生之影響，並開列其一般性的理論架權，俾能推廣於各類政策、制度施行後之 檢討與評估。 論文提要內容： 二. 研究方法： 本文所採方法論為時間序列 ARIMA干預分析模式；在函數型式方面運用BOX-COX 轉換 將 性及對數 性型式一般化，干擾項方面則採用 ARIMA模式將普通最小平方法及一 階自我迥歸的Cochrane-Orcutt 法一般化；且運用轉移函數原理將靜態迥歸及幾何落 差迥歸擴展為一般化的動態轉移函數關係。而其最大特點為除透過多解釋變數對因變 態變數之影響外，並融入虛擬變數，俾能更精確地解釋其間的動態關係。 資料方面，以民國73年1.月起至78年2.月止之台灣股價指數、貨幣供給量、匯率、躉 售物價指數與領先指標等時間序列資料，經由「內差法」(interpolation) 轉成週平 均指數來處理。 本文之研究流程如圖一所示。 三.章節結構： 本文第一章為緒論，概述研究動機與目的，並對研究方法予以簡介。第二章由理論與 實證文獻中探討干預分析模式與台灣股價之有關文獻。第三章將建立完整之理論架構 與基礎。第四章為實證分析，擬舉「證所稅」之課徵為外生干預變數，運用所建立之 理論基礎，說明實證分析步驟，並深入分析估計結果，第五章為本文結語，評估本文 所發展之一般化動態股價模式，並檢討政策或制度施行後對股市之衝擊，且由研究限 制中提出後績研究方向與建議。 四. 預期成果： 本研究預計將索性的發展一模式，此模式如果成功可有效地檢討與評估證券市場各類 政策，制度施行後之影響與衝擊。 本文所發展之一般化動態股價模式在理論上有所突破，可為爾後研究開闢另一方向。 而且以往屬質性之政策問題均乏人探討，本文預計可探討屬質性之衝擊並收拋磚引玉 之功。

時間數列分析自一九七０年Box-Jenkins 發展出自我迴歸移動平均整合模式（簡稱Ａ ＲＩＭＡ（ｐ，ｄ，ｑ））建立法後，便更普遍地應用於經濟、企管、工程及物理等 相關領域上。但利用Box-Jenkins 的鑑定方法一般只對ＭＡ或ＡＲ模型有效，而對混 合的ＡＲＭＡ模型則不適用。其後陸續有統計學者提出不同的鑑定方法，但都無法有 效地決定Ｐ、ｄ、ｑ階數。 直至一九八四年以後，Tsay和Tiao兩位學者才又提出了一套有效的鑑定法則，利用擴 展的樣本自我相關函數（Extended Sample Autocorrelation Function）或正規分析 （Canonical Analysis）求出的最小正規相關係數（The Smallest Canonical Corr- elation ）做為鑑定ｐ、ｄ、ｑ的準則。這兩種方法的優點皆為可直接處理平穩或非 平穩型時間數列，而不用事先決定差分的階數，而且對混合ＡＲＩＭＡ模型亦有效。 對於有異常點（Outlier ）存在的時間數列，其可能由於某些外在的介入因素所引起 ，而ＡＲＩＭＡ模型對資料的配適是不足夠的。因此該如何發現異常點的存在及加入 合理的介入模式亦構成了模型鑑定的問題。本文除對Tsay和Tiao的方法做一說明外， 亦利用其鑑定方法對存在有異常點的時間數列做一分析，並由實證研究探討其對季節 模型的鑑定效果。

Made available in DSpace on 2016-04-26T20:48:37Z (GMT). No. of bitstreams: 1 Emerson Oliveira de Queiros.pdf: 1394134 bytes, checksum: e8f7b40ddc426553915a8ec319148ce7 (MD5) Previous issue date: 2012-08-24 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The goal of the present work it to use the Box-Jenkins methodology, also known as ARIMA methodology, to build a short-term model of prediction for the state tax (ICMS) revenue. This tax is the most important source of income for the states in Brazil. Therefore, to predict precisely the volume of resources to be collected, other then being a legal requirement, may also be crucial for the financial management of the States. The results achieved indicate that the Box-Jenkins methodology can be a useful tool to forecast the short-term tax revenue from taxes with the characteristics of the ICMS (Value Add Tax) / O presente trabalho objetiva aplicar a metodologia Box-Jenkins, conhecida também como metodologia ARIMA, a fim de construir um modelo de previsão de curto prazo para o imposto estadual (ICMS). Trata-se de um imposto de grande peso relativo nas receitas dos estados no Brasil. Portanto, antecipar precisamente o volume de recursos advindos da principal fonte de receita dos estados no Brasil, além de ser uma imposição legal, pode ser também crucial na gestão financeira dos Estados. Os resultados indicaram que a metodologia Box-Jenkins pode ser uma ferramenta útil se a intenção for construir um modelo de previsão de curto prazo para o imposto com as características do ICMS