An investigation into the recurring patterns of forex time series data

Countless theories have been developed by both researchers and financial analyst in an attempt to explain the fluctuation of forex price. By obtaining an intimate understanding of the forex market, traders will hopefully be able to forecast and react to forex price oscillations on-the-fly towards making a profitable investment. In this paper, an investigation into the underlying theory that there exists repeating patterns within the time series data which forms the basis of technical analysis is conducted. The assumption that certain patterns do develop over time and the forex market does not fluctuate in a random manner is used to establish the fact that history repeats itself in forex trading. The patterns and repetitions unveiled within the forex historical data would be an important element for forex forecasting

Feature-based Time Series Analytics

Time series analytics is a fundamental prerequisite for decision-making as well as automation and occurs in several applications such as energy load control, weather research, and consumer behavior analysis. It encompasses time series engineering, i.e., the representation of time series exhibiting important characteristics, and data mining, i.e., the application of the representation to a specific task. Due to the exhaustive data gathering, which results from the ``Industry 4.0'' vision and its shift towards automation and digitalization, time series analytics is undergoing a revolution. Big datasets with very long time series are gathered, which is challenging for engineering techniques. Traditionally, one focus has been on raw-data-based or shape-based engineering. They assess the time series' similarity in shape, which is only suitable for short time series. Another focus has been on model-based engineering. It assesses the time series' similarity in structure, which is suitable for long time series but requires larger models or a time-consuming modeling. Feature-based engineering tackles these challenges by efficiently representing time series and comparing their similarity in structure. However, current feature-based techniques are unsatisfactory as they are designed for specific data-mining tasks. In this work, we introduce a novel feature-based engineering technique. It efficiently provides a short representation of time series, focusing on their structural similarity. Based on a design rationale, we derive important time series characteristics such as the long-term and cyclically repeated characteristics as well as distribution and correlation characteristics. Moreover, we define a feature-based distance measure for their comparison. Both the representation technique and the distance measure provide desirable properties regarding storage and runtime. Subsequently, we introduce techniques based on our feature-based engineering and apply them to important data-mining tasks such as time series generation, time series matching, time series classification, and time series clustering. First, our feature-based generation technique outperforms state-of-the-art techniques regarding the accuracy of evolved datasets. Second, with our features, a matching method retrieves a match for a time series query much faster than with current representations. Third, our features provide discriminative characteristics to classify datasets as accurately as state-of-the-art techniques, but orders of magnitude faster. Finally, our features recommend an appropriate clustering of time series which is crucial for subsequent data-mining tasks. All these techniques are assessed on datasets from the energy, weather, and economic domains, and thus, demonstrate the applicability to real-world use cases. The findings demonstrate the versatility of our feature-based engineering and suggest several courses of action in order to design and improve analytical systems for the paradigm shift of Industry 4.0

Contributions to Time Series Classification: Meta-Learning and Explainability

141 p.La presente tesis incluye 3 contribuciones de diferentes tipos al área de la clasificación supervisada de series temporales, un campo en auge por la cantidad de series temporales recolectadas día a día en una gran variedad en ámbitos. En este contexto, la cantidad de métodos disponibles para clasificar series temporales es cada vez más grande, siendo los clasificadores cada vez más competitivos y variados. De esta manera, la primera contribución de la tesis consiste en proponer una taxonomía de los clasificadores de series temporales basados en distancias, donde se hace una revisión exhaustiva de los métodos existentes y sus costes computacionales. Además, desde el punto de vista de un/a usuario/a no experto/a (incluso desde la de un/a experto/a), elegir un clasificador adecuado para un problema concreto es una tarea difícil. En la segunda contribución, por tanto, se aborda la recomendación de clasificadores de series temporales, para lo que usaremos un enfoque basado en el meta-aprendizaje. Por último, la tercera contribución consiste en proponer un método para explicar la predicción de los clasificadores de series temporales, en el que calculamos la relevancia de cada región de una serie en la predicción. Este método de explicación está basado en perturbaciones, para lo que consideraremos transformaciones específicas y realistas para las series temporales

Contributions to Time Series Classification: Meta-Learning and Explainability

This thesis includes 3 contributions of different types to the area of supervised time series classification, a growing field of research due to the amount of time series collected daily in a wide variety of domains. In this context, the number of methods available for classifying time series is increasing, and the classifiers are becoming more and more competitive and varied. Thus, the first contribution of the thesis consists of proposing a taxonomy of distance-based time series classifiers, where an exhaustive review of the existing methods and their computational costs is made. Moreover, from the point of view of a non-expert user (even from that of an expert), choosing a suitable classifier for a given problem is a difficult task. The second contribution, therefore, deals with the recommendation of time series classifiers, for which we will use a meta-learning approach. Finally, the third contribution consists of proposing a method to explain the prediction of time series classifiers, in which we calculate the relevance of each region of a series in the prediction. This method of explanation is based on perturbations, for which we will consider specific and realistic transformations for the time series.BES-2016-07689

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

Genome-scale Proteome Quantification by DEEP SEQ Mass Spectrometry

Advances in chemistry and massively parallel detection underlie DNA sequencing platforms that are poised for application in personalized medicine. In stark contrast, systematic generation of protein-level data lags well-behind genomics in virtually every aspect: depth of coverage, throughput, ease of sample preparation, and experimental time. Here, to bridge this gap, we develop an approach based on simple detergent lysis and single-enzyme digest, extreme, orthogonal separation of peptides, and true nanoflow LC-MS/MS that provides high peak capacity and ionization efficiency. This automated, deep efficient peptide sequencing and quantification (DEEP SEQ) mass spectrometry platform provides genome-scale proteome coverage equivalent to RNA-seq ribosomal profiling and accurate quantification for multiplexed isotope labels. In a model of the embryonic to epiblast transition in murine stem cells, we unambiguously quantify 11,352 gene products that span 70% of Swiss-Prot and capture protein regulation across the full detectable range of high-throughput gene expression and protein translation

Features Extraction from Time Series

Time series can be found in various domains like medicine, engineering, and finance. Generally speaking, a time series is a sequence of data that represents recorded values of a phenomenon over time. This thesis studies time series mining, including transformation and distance measure, anomaly or anomalies detection, clustering and remaining useful life estimation. In the course of the first mining task (transformation and distance measure), in order to increase the accuracy of distance measure between transformed series (symbolic series), we introduce a novel calculation of distance between symbols. By integrating this newly defined method to symbolic aggregate approximation and its extensions, the experimental results show this proposed method is promising. During the process of the second mining task (anomaly or anomalies detection), for the purpose of improving the accuracy of anomaly or anomalies detection, we propose a distance measure method and an anomalies detection calculation. These proposed methods, together with previous published anomaly detection methods, are applied to real ECG data selected from MIT-BIH database. The experimental results show that our proposed outperforms other methods. During the course of the third mining task (clustering), we present an automatic clustering method, called AT-means, which can automatically carry out clustering for a given time series dataset: from the calculation of global average time series to the setting of initial centres and the determination of the number of clusters. The performance of the proposed method was tested on 10 benchmark time series datasets obtained from UCR database. For comparison, the K-means method with three different conditions are also applied to the same datasets. The experimental results show the proposed method outperforms the compared K-means approaches. During the process of the fourth mining task (remaining useful life estimation), all the original data are transformed into low-dimensional space through principal components analysis. We then proposed a novel multidimensional time series distance measure method, called as multivariate time series warping distance (MTWD), for remaining useful life estimation. This whole process is tested on the CMAPSS (Commercial Modular Aero Propulsion System Simulation) datasets and the performance is compared with two existing methods. The experimental results show that the estimated remaining useful life (RUL) values are closer to real RUL values when compared with the comparison methods. Our work contributes to the time series mining by introducing novel approaches to distance measure, anomalies detection, clustering and RUL estimation. We furthermore apply our proposed methods and related methods to benchmark datasets. The experimental results show that our methods are better than previously published methods in terms of accuracy and efficiency

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

FAULT DETECTION AND PREDICTION IN ELECTROMECHANICAL SYSTEMS VIA THE DISCRETIZED STATE VECTOR-BASED PATTERN ANALYSIS OF MULTI-SENSOR SIGNALS

Department of System Design and Control EngineeringIn recent decades, operation and maintenance strategies for industrial applications have evolved from corrective maintenance and preventive maintenance, to condition-based monitoring and eventually predictive maintenance. High performance sensors and data logging technologies have enabled us to monitor the operational states of systems and predict fault occurrences. Several time series analysis methods have been proposed in the literature to classify system states via multi-sensor signals. Since the time series of sensor signals is often characterized as very-short, intermittent, transient, highly nonlinear, and non-stationary random signals, they make time series analyses more complex. Therefore, time series discretization has been popularly applied to extract meaningful features from original complex signals. There are several important issues to be addressed in discretization for fault detection and prediction: (i) What is the fault pattern that represents a system???s faulty states, (ii) How can we effectively search for fault patterns, (iii) What is a symptom pattern to predict fault occurrences, and (iv) What is a systematic procedure for online fault detection and prediction. In this regard, this study proposes a fault detection and prediction framework that consists of (i) definition of system???s operational states, (ii) definitions of fault and symptom patterns, (iii) multivariate discretization, (iv) severity and criticality analyses, and (v) online detection and prediction procedures. Given the time markers of fault occurrences, we can divide a system???s operational states into fault and no-fault states. We postulate that a symptom state precedes the occurrence of a fault within a certain time period and hence a no-fault state consists of normal and symptom states. Fault patterns are therefore found only in fault states, whereas symptom patterns are either only found in the system???s symptom states (being absent in the normal states) or not found in the given time series, but similar to fault patterns. To determine the length of a symptom state, we present a symptom pattern-based iterative search method. In order to identify the distinctive behaviors of multi-sensor signals, we propose a multivariate discretization approach that consists mainly of label definition, label specification, and event codification. Discretization parameters are delicately controlled by considering the key characteristics of multi-sensor signals. We discuss how to measure the severity degrees of fault and symptom patterns, and how to assess the criticalities of fault states. We apply the fault and symptom pattern extraction and severity assessment methods to online fault detection and prediction. Finally, we demonstrate the performance of the proposed framework through the following six case studies: abnormal cylinder temperature in a marine diesel engine, automotive gasoline engine knockings, laser weld defects, buzz, squeak, and rattle (BSR) noises from a car door trim (using a typical acoustic sensor array and using acoustic emission sensors respectively), and visual stimuli cognition tests by the P300 experiment.ope