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11	Ανάλυση των χρηματιστηριακών δεδομένων με χρήση των αλγορίθμων εξόρυξης Μπεγκόμ, Τζαχίντα 10 June 2014 (has links) Λόγω της έξαρσης της τεχνολογικής ανάπτυξης ο όγκος των πληροφοριών σήμερα είναι τεράστιος και έχει δημιουργήσει την ανάγκη για την ανάλυση και την επεξεργασία των δεδομένων ώστε, μετά την επεξεργασία, να μπορούν να μετατραπούν σε χρήσιμες πληροφορίες και να μας βοηθήσουν στη λήψη αποφάσεων. Οι τεχνικές εξόρυξης δεδομένων σε συνδυασμό με τις στατιστικές μεθόδους αποτελούν σπουδαίο εργαλείο για την ανάκτηση των συγκεκριμένων πληροφοριών. Η χρήση αυτών των πληροφοριών βοηθά στη μελέτη και κατ’επέκταση στην εξαγωγή των συμπερασμάτων για το χαρακτηριστικό που εξετάζεται. Ένας τομέας που παρουσιάζει μεγάλο ερευνητικό ενδιαφέρον, λόγω του όγκου των πληροφοριών που συσσωρεύει καθημερινά, είναι το χρηματιστήριο. Η εξόρυξη γνώσης από τα δεδομένα με σκοπό την όσο το δυνατόν «σωστή» πρόβλεψη μπορεί να αποφέρει πολύ μεγάλο κέρδος και αυτός είναι ένας λόγος για τον οποίο πολλές επιχειρήσεις έχουν επενδύσει στην τεχνολογία των πληροφοριών.Η παρούσα εργασία εδράζεται στο πλαίσιο της γενικής προσπάθειας τεχνικής ανάλυσης χρηματιστηριακών δεδομένων, εστιάζοντας παράλληλα στην ανάλυση με τη χρήση τεχνικών εξόρυξης. Το αντικείμενο της παρούσας διπλωματικής εργασίας είναι η ανάλυση των χρηματιστηριακών δεδομένων (χρονοσειρών) χρησιμοποιώντας τεχνικές εξόρυξης που μπορούν να βοηθήσουν στη λήψη των αποφάσεων. Συγκεκριμένα, στους στόχους της εργασίας περιλαμβάνεται η ομαδοποίηση παρόμοιων μετοχών, η εύρεση της κατηγορίας των μετοχών στην οποία μπορεί να ανήκει μία νέα μετοχή και η πρόβλεψη των μελλοντικών τιμών. Οι μελέτες αυτές εκτός από το χρηματιστήριο, μπορούν να εφαρμοστούν επίσης για την αναγνώριση των προτύπων, τη διαχείριση του χαρτοφυλακίου και τις χρηματοπιστωτικές αγορές. / The rapid development of technology has led to a large increase in the volume of information, creating the need for data analysis and processing. After processing, these data can be transformed into useful information that can help us to make decisions. The data mining techniques combined with the statistical methods are important tools for the recovery of such information. This information helps us to study the features and to extract information about them. The stock market is one of the greatest research areas of interest due to the volume of the information that accumulates daily. Knowledge extraction from data aiming the best possible prediction could yield significant profit, thus making information technology a magnet for corporate investment. This thesis is based on the general effort of technical analysis for stock market data, while focusing on analysis using data mining techniques. The present thesis aims to analyze stock data (time series) by applying data mining techniques which enable decision making. Specifically, the objectives of the work include the grouping of similar stocks, the determination of the class in which a new stock may belong and the prediction of the closing values of the stocks. Apart from the stock market, these studies can also be applied for the pattern recognition, portfolio management and financial markets. Εξόρυξη δεδομένων Συσταδοποίηση Κατηγοριοποίηση Πρόβλεψη 332.102 856 3 Data mining Stock data Clustering Classification Prediction Time series modeling
12	Software for Manipulating and Embedding Data Interrogation Algorithms Into Integrated Systems Allen, David W. 20 January 2005 (has links) In this study a software package for easily creating and embedding structural health monitoring (SHM) data interrogation processes in remote hardware is presented. The software described herein is comprised of two pieces. The first is a client to allow graphical construction of data interrogation processes. The second is node software for remote execution of processes on remote sensing and monitoring hardware. The client software is created around a catalog of data interrogation algorithms compiled over several years of research at Los Alamos National Laboratory known as DIAMOND II. This study also includes encapsulating the DIAMOND II algorithms into independent interchangeable functions and expanding the catalog with work in feature extraction and statistical discrimination. The client software also includes methods for interfacing with the node software over an Internet connection. Once connected, the client software can upload a developed process to the integrated sensing and processing node. The node software has the ability to run the processes and return results. This software creates a distributed SHM network without individual nodes relying on each other or a centralized server to monitor a structure. For the demonstration summarized in this study, the client software is used to create data collection, feature extraction, and statistical modeling processes. Data are collected from monitoring hardware connected to the client by a local area network. A structural health monitoring process is created on the client and uploaded to the node software residing on the monitoring hardware. The node software runs the process and monitors a test structure for induced damage, returning the current structural-state indicator in near real time to the client. Current integrated health monitoring systems rely on processes statically loaded onto the monitoring node before the node is deployed in the field. The primary new contribution of this study is a software paradigm that allows processes to be created remotely and uploaded to the node in a dynamic fashion over the life of the monitoring node without taking the node out of service. / Master of Science Extreme Value Statistics Integrated Systems Data Interrogation Structural Health Monitoring Embedded Systems JAVA Software Time Series Modeling Process Development Statistical Discrimination
13	Predictability of Nonstationary Time Series using Wavelet and Empirical Mode Decomposition Based ARMA Models Lanka, Karthikeyan January 2013 (has links) (PDF) The idea of time series forecasting techniques is that the past has certain information about future. So, the question of how the information is encoded in the past can be interpreted and later used to extrapolate events of future constitute the crux of time series analysis and forecasting. Several methods such as qualitative techniques (e.g., Delphi method), causal techniques (e.g., least squares regression), quantitative techniques (e.g., smoothing method, time series models) have been developed in the past in which the concept lies in establishing a model either theoretically or mathematically from past observations and estimate future from it. Of all the models, time series methods such as autoregressive moving average (ARMA) process have gained popularity because of their simplicity in implementation and accuracy in obtaining forecasts. But, these models were formulated based on certain properties that a time series is assumed to possess. Classical decomposition techniques were developed to supplement the requirements of time series models. These methods try to define a time series in terms of simple patterns called trend, cyclical and seasonal patterns along with noise. So, the idea of decomposing a time series into component patterns, later modeling each component using forecasting processes and finally combining the component forecasts to obtain actual time series predictions yielded superior performance over standard forecasting techniques. All these methods involve basic principle of moving average computation. But, the developed classical decomposition methods are disadvantageous in terms of containing fixed number of components for any time series, data independent decompositions. During moving average computation, edges of time series might not get modeled properly which affects long range forecasting. So, these issues are to be addressed by more efficient and advanced decomposition techniques such as Wavelets and Empirical Mode Decomposition (EMD). Wavelets and EMD are some of the most innovative concepts considered in time series analysis and are focused on processing nonlinear and nonstationary time series. Hence, this research has been undertaken to ascertain the predictability of nonstationary time series using wavelet and Empirical Mode Decomposition (EMD) based ARMA models. The development of wavelets has been made based on concepts of Fourier analysis and Window Fourier Transform. In accordance with this, initially, the necessity of involving the advent of wavelets has been presented. This is followed by the discussion regarding the advantages that are provided by wavelets. Primarily, the wavelets were defined in the sense of continuous time series. Later, in order to match the real world requirements, wavelets analysis has been defined in discrete scenario which is called as Discrete Wavelet Transform (DWT). The current thesis utilized DWT for performing time series decomposition. The detailed discussion regarding the theory behind time series decomposition is presented in the thesis. This is followed by description regarding mathematical viewpoint of time series decomposition using DWT, which involves decomposition algorithm. EMD also comes under same class as wavelets in the consequence of time series decomposition. EMD is developed out of the fact that most of the time series in nature contain multiple frequencies leading to existence of different scales simultaneously. This method, when compared to standard Fourier analysis and wavelet algorithms, has greater scope of adaptation in processing various nonstationary time series. The method involves decomposing any complicated time series into a very small number of finite empirical modes (IMFs-Intrinsic Mode Functions), where each mode contains information of the original time series. The algorithm of time series decomposition using EMD is presented post conceptual elucidation in the current thesis. Later, the proposed time series forecasting algorithm that couples EMD and ARMA model is presented that even considers the number of time steps ahead of which forecasting needs to be performed. In order to test the methodologies of wavelet and EMD based algorithms for prediction of time series with non stationarity, series of streamflow data from USA and rainfall data from India are used in the study. Four non-stationary streamflow sites (USGS data resources) of monthly total volumes and two non-stationary gridded rainfall sites (IMD) of monthly total rainfall are considered for the study. The predictability by the proposed algorithm is checked in two scenarios, first being six months ahead forecast and the second being twelve months ahead forecast. Normalized Root Mean Square Error (NRMSE) and Nash Sutcliffe Efficiency Index (Ef) are considered to evaluate the performance of the proposed techniques. Based on the performance measures, the results indicate that wavelet based analyses generate good variations in the case of six months ahead forecast maintaining harmony with the observed values at most of the sites. Although the methods are observed to capture the minima of the time series effectively both in the case of six and twelve months ahead predictions, better forecasts are obtained with wavelet based method over EMD based method in the case of twelve months ahead predictions. It is therefore inferred that wavelet based method has better prediction capabilities over EMD based method despite some of the limitations of time series methods and the manner in which decomposition takes place. Finally, the study concludes that the wavelet based time series algorithm could be used to model events such as droughts with reasonable accuracy. Also, some modifications that could be made in the model have been suggested which can extend the scope of applicability to other areas in the field of hydrology. Time Series Forecasting Nonstationary Time Series Wavelets Time Series Decomposition Algorithms Autoregressive Moving Average Model Time Series Modeling Time Series Analysis Streamflow Rain and Rainfall Time Series Nonstationarity Drought Forecasting Hydrology - Time Series Modeling Empirical Mode Decomposition Model Continuous Wavelet Transforms (CWT) Time Series Forecasting Techniques Discrete Wavelet Transform (DWT) Empirical Mode Decomposition (EMD) Civil Engineering
14	Evoluční predikce časových řad / Evolutionary Prediction of Time Series Křivánek, Jan January 2009 (has links) This thesis summarizes knowledge in the field of time series theory, method for time series analysis and applications in financial modeling. It also resumes the area of evolutionary algorithms, their classification and applications. The core of this work combines these knowledges in order to build a system utilizing evolutionary algorithms for financial time series forecasting models optimization. Various software engineering techniques were used during the implementation phase (ACI - autonomous continual integration, autonomous quality control etc.) to ensure easy maintainability and extendibility of project by more developers.
15	Football Trajectory Modeling Using Masked Autoencoders : Using Masked Autoencoder for Anomaly Detection and Correction for Football Trajectories / Modellering av Fotbollsbana med Maskerade Autoencoders : Maskerade Autoencoders för Avvikelsedetektering och Korrigering av Fotbollsbanor Tor, Sandra January 2023 (has links) Football trajectory modeling is a powerful tool for predicting and evaluating the movement of a football and its dynamics. Masked autoencoders are scalable self-supervised learners used for representation learning of partially observable data. Masked autoencoders have been shown to provide successful results in pre-training for computer vision and natural language processing tasks. Using masked autoencoders in the multivariate time-series data field has not been researched to the same extent. This thesis aims to investigate the potential of using masked autoencoders for multivariate time-series modeling for football trajectory data in collaboration with Tracab. Two versions of the masked autoencoder network with alterations are tested, which are implemented to be used with multivariate time-series data. The resulting models are used to detect anomalies in the football trajectory and propose corrections based on the reconstruction. The results are evaluated, discussed, and compared against the tracked and manually corrected value of the ball trajectory. The performance of the different frameworks is compared and the overall anomaly detection capabilities are discussed. The result suggested that even though the regular autoencoder version had a smaller average reconstruction error during training and testing, using masked autoencoders improved the anomaly detection performance. The result suggested that neither the regular autoencoder nor the masked autoencoder managed to propose plausible trajectories to correct anomalies in the data. This thesis promotes further research to be done in the field of using masked autoencoders for time series and trajectory modeling. / Modellering av en fotbolls bollbana är ett kraftfullt verktyg för att förutse och utvärdera rörelsen och dynamiken hos en fotboll. Maskerade autoencoders är skalbara självövervakande inlärare som används för representationsinlärning av delvis synlig data. Maskerade autoencoders har visat sig ge framgångsrika resultat vid förträning inom datorseende och naturlig språkbearbetning. Användningen av maskerade autoencoders för multivariat tidsserie-data har det inte forskats om i samma omfattning. Syftet med detta examensarbete är att undersöka potentialen för maskerade autoencoders inom tidsseriemodellering av bollbanor för fotboll i samarbete med Tracab. Två versioner av maskerade autoencoders anpassade för tidsserier testas. De tränade modellerna används för att upptäcka avvikelser i detekterade fotbollsbanor och föreslå korrigeringar baserat på rekonstruktionen. Resultaten utvärderas, diskuteras och jämförs med det detekterade och manuellt korrigerade värdet för fotbollens bollbana. De olika ramverken jämförs och deras förmåga för detektion och korrigering av avvikelser diskuteras. Resultatet visade att även om den vanliga autoencoder-versionen hade ett mindre genomsnittligt rekonstruktionsfel efter träning, så bidrog användningen av maskerade autoencoders till en förbättring inom detektering av avvikelser. Resultatet visade att varken den vanliga autoencodern eller den maskerade autoencodern lyckades föreslå trovärdiga bollbanor för att korrigera de funna avvikelserna i datan. Detta examensarbete främjar ytterligare forskning inom användningen av maskerade autoencoders för tidsserier och banmodellering. Machine Learning Autoencoders Masked autoencoders Time series Trajectory modeling Time series modeling Anomaly detection Anomaly correction Football Maskininlärning Autoencoders Maskerade autoencoders Tidsserie Banmodellering Tidsseriemodellering Avvikelsedetektering Avvikelsekorrigering Fotboll Computer and Information Sciences Data- och informationsvetenskap
16	Utvecklingen av marknadsvärdet för svenska frekvenshållningsreserver 2024–2030 : En prognos för utvecklingen av marknadsvärdet för frekvenshållningsreserverna FCR-N, FCR-D upp och FCR-D ned på den svenska balansmarknaden mellan 2024 och 2030 / The Development of the Market Value of Swedish Frequency Containment Reserves 2024–2030 : A forecast for the development of the market value for the frequency containment reserves FCR-N, FCR-D up and FCR-D down in the Swedish balancing market between 2024 and 2030 Ludvig, Aldén, Gustav, Espefält, Gabriel, Gabro January 2024 (has links) I takt med en ökad andel variabel förnybar elproduktion i Sveriges energimix blir elnätets flexibilitet allt viktigare för att upprätthålla en stabil elförsörjning. Detta arbete undersöker framtida prognoser för priser och volymer på de svenska frekvenshållningsreserverna FCR-N, FCR-D upp och FCR-D ned fram till år 2030. Prognoser för sådan utveckling är viktiga för elmarknadens aktörer och deras beslut att investera i flexibilitetsresurser. SARIMAX-modeller utvecklades baserade på historisk data och antaganden om framtida utvecklingar, vilka i sin tur grundades på en intervju med en branschexpert samt aktuella kartläggningar och rapporter. Resultaten visar på en markant nedåtgående pristrend. För FCR-N prognostiseras priserna sjunka med 367 % från 2024 till 2030, från 29 euro/MW till 5 euro/MW. FCR-D upp förväntas följa en liknande trend med ett prisfall på 325 %, från 20 euro/MW år 2024 till 4 euro/MW år 2030. Den kraftigaste prisnedgången prognostiseras för FCR-D ned, där priserna beräknas rasa med över 1900 % under samma period - från 61 euro/MW år 2024 till endast 3 euro/MW år 2030. Vad gäller volymer visar prognoserna på en relativt stabil utveckling kring upphandlingsplanerna, med en viss ökning för FCR-D ned på 44 % från 2024 till 2030. Den pågående etableringen av batterilager förutses ha stor påverkan genom att öka konkurrensen och pressa priserna nedåt. De låga prisnivåerna 2030 kan dock göra det utmanande att motivera investeringar enbart baserat på intäkter från FCR-marknader. Vidare diskuteras modellernas begränsningar samt behovet av framtida forskning kring batteriteknik, råvaruaspekter och avancerade simuleringsmodeller för att bättre förstå marknadsdynamiken. / As the share of variable renewable electricity production increases in Sweden's energy mix, the flexibility of the power grid becomes increasingly important to maintain a stable electricity supply. This study aims to forecast prices and volumes of the Swedish frequency containment reserves FCR-N, FCR-D up, and FCR-D down until 2030. Forecasts of such developments are important for electricity market participants and their decisions to invest in flexibility resources. SARIMAX models were developed based on historical data and assumptions about future developments, which in turn were based on an interview with an industry expert as well as current reports. The results indicate a significant downward price trend. For FCR-N, prices are forecasted to decrease by 367% from 2024 to 2030, dropping from 29 euros/MW to 5 euros/MW. FCR-D up is expected to follow a similar trend with a 325% price drop, from 20 euros/MW in 2024 to 4 euros/MW in 2030. The sharpest price decline is forecasted for FCR-D down, where prices are estimated to plummet by over 1900% during the same period - from 61 euros/MW in 2024 to only 3 euros/MW in 2030. Regarding volumes, the forecasts show a relatively stable development around the procurement plans, with a certain increase for FCR-D down by 44% from 2024 to 2030. The ongoing establishment of battery storage is expected to have a major impact by increasing competition and putting downward pressure on prices. However, the low price levels in 2030 may make it challenging to justify investments based solely on revenues from FCR markets. Furthermore, the limitations of the models are discussed, as well as the need for future research on battery technology, raw material aspects, and advanced simulation models to better understand market dynamics. FCR frequency containment reserves FCR-N FCR-D up FCR-D down upregulation downregulation variable renewable energy VRE power grid power system flexibility electricity supply stable forecast electricity market SARIMAX time series modeling data price volume market value energy storage battery storage battery park batteries competition time series flexibility resource flexibility service risk system effect future scenarios scenario balancing market volume development investment decisions scenario analysis uncertainty management sensitivity analysis competitive situation commodity prices technology development policy impact capacity expansion profitability calculations risk management FCR frekvenshållningsreserver FCR-N FCR-D upp FCR-D ned uppreglering nedreglering variabel förnybar energiproduktion VRE elnät kraftsystem flexibilitet elförsörjning stabil prognos elmarknad SARIMAX tidsseriemodellering data pris volym marknadsvärde energilagring batterilager batteripark batterier konkurrens tidsserie flexibilitetsresurs flexibilitetstjänst risk systemeffekt framtidsscenarier scenario balansmarknad volymutveckling investeringsbeslut scenarioanalys osäkerhetshantering känslighetsanalys konkurrenssituation råvarupriser teknologiutveckling policypåverkan kapacitetsutbyggnad lönsamhetsberäkningar riskhantering Energy Systems Energisystem

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