Vector Autoregresive Moving Average Identification for Macroeconomic Modeling: Algorithms and Theory

This paper develops a new methodology for identifying the structure of VARMA time series models. The analysis proceeds by examining the echelon canonical form and presents a fully automatic data driven approach to model specification using a new technique to determine the Kronecker invariants. A novel feature of the inferential procedures developed here is that they work in terms of a canonical scalar ARMAX representation in which the exogenous regressors are given by predetermined contemporaneous and lagged values of other variables in the VARMA system. This feature facilitates the construction of algorithms which, from the perspective of macroeconomic modeling, are efficacious in that they do not use AR approximations at any stage. Algorithms that are applicable to both asymptotically stationary and unit-root, partially nonstationary (cointegrated) time series models are presented. A sequence of lemmas and theorems show that the algorithms are based on calculations that yield strongly consistent estimates.Keywords: Algorithms, asymptotically stationary and cointegrated time series, echelon

A quantified past : fieldwork and design for remembering a data-driven life

PhD ThesisA ‘data-driven life’ has become an established feature of present and future technological visions. Smart homes, smart cities, an Internet of Things, and particularly the Quantified Self movement are all premised on the pervasive datafication of many aspects of everyday life. This thesis interrogates the human experience of such a data-driven life, by conceptualising, investigating, and speculating about these personal informatics tools as new technologies of memory. With respect to existing discourses in Human-Computer Interaction, Memory Studies and Critical Data Studies, I argue that the prevalence of quantified data and metrics is creating fundamentally new and distinct records of everyday life: a quantified past. To address this, I first conduct qualitative, and idiographic fieldwork – with long-term self-trackers, and subsequently with users of ‘smart journals’ – to investigate how this data-driven record mediates the experience of remembering. Further, I undertake a speculative and design-led inquiry to explore context of a ’quantified wedding’. Adopting a context where remembering is centrally valued, this Research through Design project demonstrates opportunities and develops considerations for the design of data-driven tools for remembering. Crucially, while speculative, this project maintains a central focus on individual experience, and introduces an innovative methodological approach ‘Speculative Enactments’ for engaging participants meaningfully in speculative inquiry. The outcomes of this conceptual, empirical and speculative inquiry are multiple. I present, and interpret, a variety of rich descriptions of existing and anticipated practices of remembering with data. Introducing six experiential qualities of data, and reflecting on how data requires selectivity and construction to meaningfully account for one’s life, I argue for the design of ‘Documentary Informatics’. This perspective fundamentally reimagines the roles and possibilities for personal informatics tools; it looks beyond the current present-focused and goal-oriented paradigm of a data-driven life, to propose a more poetic orientation to recording one’s life with quantified data

Numerical Bifurcation Analysis of PDEs From Lattice Boltzmann Model Simulations: a Parsimonious Machine Learning Approach

We address a three-tier data-driven approach for the numerical solution of the inverse problem in Partial Differential Equations (PDEs) and for their numerical bifurcation analysis from spatio-temporal data produced by Lattice Boltzmann model simulations using machine learning. In the first step, we exploit manifold learning and in particular parsimonious Diffusion Maps using leave-one-out cross-validation (LOOCV) to both identify the intrinsic dimension of the manifold where the emergent dynamics evolve and for feature selection over the parameter space. In the second step, based on the selected features, we learn the right-hand-side of the effective PDEs using two machine learning schemes, namely shallow Feedforward Neural Networks (FNNs) with two hidden layers and single-layer Random Projection Networks (RPNNs), which basis functions are constructed using an appropriate random sampling approach. Finally, based on the learned black-box PDE model, we construct the corresponding bifurcation diagram, thus exploiting the numerical bifurcation analysis toolkit. For our illustrations, we implemented the proposed method to perform numerical bifurcation analysis of the 1D FitzHugh-Nagumo PDEs from data generated by D1Q3 Lattice Boltzmann simulations. The proposed method was quite effective in terms of numerical accuracy regarding the construction of the coarse-scale bifurcation diagram. Furthermore, the proposed RPNN scheme was ∼ 20 to 30 times less costly regarding the training phase than the traditional shallow FNNs, thus arising as a promising alternative to deep learning for the data-driven numerical solution of the inverse problem for high-dimensional PDEs

Radiomics risk modelling using machine learning algorithms for personalised radiation oncology

One major objective in radiation oncology is the personalisation of cancer treatment. The implementation of this concept requires the identification of biomarkers, which precisely predict therapy outcome. Besides molecular characterisation of tumours, a new approach known as radiomics aims to characterise tumours using imaging data. In the context of the presented thesis, radiomics was established at OncoRay to improve the performance of imaging-based risk models. Two software-based frameworks were developed for image feature computation and risk model construction. A novel data-driven approach for the correction of intensity non-uniformity in magnetic resonance imaging data was evolved to improve image quality prior to feature computation. Further, different feature selection methods and machine learning algorithms for time-to-event survival data were evaluated to identify suitable algorithms for radiomics risk modelling. An improved model performance could be demonstrated using computed tomography data, which were acquired during the course of treatment. Subsequently tumour sub-volumes were analysed and it was shown that the tumour rim contains the most relevant prognostic information compared to the corresponding core. The incorporation of such spatial diversity information is a promising way to improve the performance of risk models.:1. Introduction 2. Theoretical background 2.1. Basic physical principles of image modalities 2.1.1. Computed tomography 2.1.2. Magnetic resonance imaging 2.2. Basic principles of survival analyses 2.2.1. Semi-parametric survival models 2.2.2. Full-parametric survival models 2.3. Radiomics risk modelling 2.3.1. Feature computation framework 2.3.2. Risk modelling framework 2.4. Performance assessments 2.5. Feature selection methods and machine learning algorithms 2.5.1. Feature selection methods 2.5.2. Machine learning algorithms 3. A physical correction model for automatic correction of intensity non-uniformity in magnetic resonance imaging 3.1. Intensity non-uniformity correction methods 3.2. Physical correction model 3.2.1. Correction strategy and model definition 3.2.2. Model parameter constraints 3.3. Experiments 3.3.1. Phantom and simulated brain data set 3.3.2. Clinical brain data set 3.3.3. Abdominal data set 3.4. Summary and discussion 4. Comparison of feature selection methods and machine learning algorithms for radiomics time-to-event survival models 4.1. Motivation 4.2. Patient cohort and experimental design 4.2.1. Characteristics of patient cohort 4.2.2. Experimental design 4.3. Results of feature selection methods and machine learning algorithms evaluation 4.4. Summary and discussion 5. Characterisation of tumour phenotype using computed tomography imaging during treatment 5.1. Motivation 5.2. Patient cohort and experimental design 5.2.1. Characteristics of patient cohort 5.2.2. Experimental design 5.3. Results of computed tomography imaging during treatment 5.4. Summary and discussion 6. Tumour phenotype characterisation using tumour sub-volumes 6.1. Motivation 6.2. Patient cohort and experimental design 6.2.1. Characteristics of patient cohorts 6.2.2. Experimental design 6.3. Results of tumour sub-volumes evaluation 6.4. Summary and discussion 7. Summary and further perspectives 8. Zusammenfassun

Eigen-spectrograms: an interpretable feature space for bearing fault diagnosis based on artificial intelligence and image processing

The Intelligent Fault Diagnosis of rotating machinery proposes some captivating challenges in light of the imminent big data era. Although results achieved by artificial intelligence and deep learning constantly improve, this field is characterized by several open issues. Models' interpretation is still buried under the foundations of data driven science, thus requiring attention to the development of new opportunities also for machine learning theories. This study proposes a machine learning diagnosis model, based on intelligent spectrogram recognition, via image processing. The approach is characterized by the introduction of the eigen-spectrograms and randomized linear algebra in fault diagnosis. The eigen-spectrograms hierarchically display inherent structures underlying spectrogram images. Also, different combinations of eigen-spectrograms are expected to describe multiple machine health states. Randomized algebra and eigen-spectrograms enable the construction of a significant feature space, which nonetheless emerges as a viable device to explore models' interpretations. The computational efficiency of randomized approaches further collocates this methodology in the big data perspective and provides new reading keys of well-established statistical learning theories, such as the Support Vector Machine (SVM). The conjunction of randomized algebra and Support Vector Machine for spectrogram recognition shows to be extremely accurate and efficient as compared to state of the art results.Comment: 14 pages, 13 figure

Unsupervised fault detection and prediction of remaining useful life for online prognostic health management of mechanical systems

Predictive maintenance allows industries to keep their production systems available as much as possible. Reducing unforeseen shutdowns to a level that is close to zero has numerous advantages, including production cost savings, a high quality level of both products and processes, and a high safety level. Studies in this field have focused on a novel approach, prognostic health management (PHM), which relies on condition monitoring (CM) for predicting the remaining useful life (RUL) of a system. However, several issues remain in its application to real industrial contexts, e.g., the difficulties in conducting tests simulating each fault condition, the dynamic nature of industrial environments, and the need to handle large amounts of data collected from machinery. In this paper, a data-driven methodology for PHM implementation is proposed, which has the following characteristics: it is unsupervised, i.e., it does not require any prior knowledge regarding fault behaviors and it does not rely on pre-trained classification models, i.e., it can be applied "from scratch"; it can be applied online due to its low computational effort, which makes it suitable for edge computing; and, it includes all of the steps that are involved in a prognostic program, i.e., feature extraction, health indicator (HI) construction, health stage (HS) division, degradation modelling, and RUL prediction. Finally, the proposed methodology is applied in this study to a rotating component. The study results, in terms of the ability of the proposed approach to make a timely prediction of component fault conditions, are promising

Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling

Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the machine learning process, yielding regional model features whose construction is driven by model prediction performance rather than prior assumptions. Our results demonstrate that Genetic Programming is particularly well suited to this type of feature construction because it can automatically synthesize appropriate aggregations, as well as better incorporate them into predictive models compared to other regression methods we tested. In our experiments we consider a specific problem instance and real-world dataset relevant to predicting snow properties in high-mountain Asia