Forecasting Financial Time Series using Linear Predictive Filters

Forecasting financial time series is regarded as one of the most challenging applications of time series prediction due to their dynamic nature. However, it is the fundamental element of most investment activities thus attracting the attention of practitioners and researchers for many decades. The purpose of this research is to investigate and develop novel methods for the prediction of financial time series considering their dynamic nature. The predictive performance of asset prices time series themselves is exploited by applying digital signal processing methods to their historical observations. The novelty of the research lies in the design of predictive filters by maximising their spectrum flatness of forecast errors. The filters are then applied to forecast linear combinations of daily open, high, low and close prices of financial time series. Given the assumption that there are no structural breaks or switching regimes in a time series, the sufficient and necessary conditions that a time series can be predicted with zero errors by linear filters are examined. It is concluded that a band-limited time series can be predicted with zero errors by a predictive filter that has a constant magnitude response and constant group delay over the bandwidth of the time series. Because real world time series are not band-limited thus cannot be forecasted without errors, statistical tests of spectrum flatness which evaluate the departure of the spectral density from a constant value are introduced as measures of the predictability of time series. Properties of a time series are then investigated in the frequency domain using its spectrum flatness. A predictive filter is designed by maximising the error spectrum flatness that is equivalent to maximise the “whiteness” of forecast errors in the frequency domain. The focus is then placed on forecasting real world financial time series. By applying spectrum flatness tests, it is found that the property of the spectrum of a linear combination of daily open, high, low and close prices, which is called target prices, is different from that of a random walk process as there are much more low frequency components than high frequency ones in its spectrum. Therefore, an objective function is proposed to derive the target price time series from the historical observations of daily open, high, low and close prices. A predictive filter is then applied to obtain the one-step ahead forecast of the target prices, while profitable trading strategies are designed based on the forecast of target prices series. As a result, more than 70% success ratio could be achieved in terms of one-step ahead out-of-sample forecast of direction changes of the target price time series by taking the S&P500 index for example

Multistage Multiscale Inference Network with Visibility Attention for Occluded Person Re-Identification

For occluded person re-identification this thesis presents the Multistage Multiscale Inference Network (MMI-Net) that leverages an inference framework based on multiscale representations with visibility guidance. MMI-Net consists of three sub-networks, i) global, ii) part-based and iii) integrated, to infer person re-identification. The global inference sub-network provides an overall holistic analysis of input images. The part-based sub-network captures more localized information. Both the global and part-based models make use of multiscale representation across multiple processing stages to capture a variety of complementary discriminative image structure. The integrated sub-network aggregates the global and part-based representations to obtain the final fusion of all extracted information. Pose guided attentional processing is used to provide robustness to occlusion. MMI-Net is unique in its integrated multistage inference architecture that accounts for local and global appearance with attentional processing. In empirical evaluation, MMI-Net outperforms current existing methods on multiple occluded person re-identification datasets

Dynamic Modeling, Sensor Placement Design, and Fault Diagnosis of Nuclear Desalination Systems

Fault diagnosis of sensors, devices, and equipment is an important topic in the nuclear industry for effective and continuous operation of nuclear power plants. All the fault diagnostic approaches depend critically on the sensors that measure important process variables. Whenever a process encounters a fault, the effect of the fault is propagated to some or all the process variables. The ability of the sensor network to detect and isolate failure modes and anomalous conditions is crucial for the effectiveness of a fault detection and isolation (FDI) system. However, the emphasis of most fault diagnostic approaches found in the literature is primarily on the procedures for performing FDI using a given set of sensors. Little attention has been given to actual sensor allocation for achieving the efficient FDI performance. This dissertation presents a graph-based approach that serves as a solution for the optimization of sensor placement to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. This would potentially facilitate an automated sensor allocation procedure. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data, and to fit a hyper-plane to the data. The fault directions for different fault scenarios are obtained from the prediction errors, and fault isolation is then accomplished using new projections on these fault directions. The effectiveness of the use of an optimal sensor set versus a reduced set for fault detection and isolation is demonstrated using this technique. Among a variety of desalination technologies, the multi-stage flash (MSF) processes contribute substantially to the desalinating capacity in the world. In this dissertation, both steady-state and dynamic simulation models of a MSF desalination plant are developed. The dynamic MSF model is coupled with a previously developed International Reactor Innovative and Secure (IRIS) model in the SIMULINK environment. The developed sensor placement design and fault diagnostic methods are illustrated with application to the coupled nuclear desalination system. The results demonstrate the effectiveness of the newly developed integrated approach to performance monitoring and fault diagnosis with optimized sensor placement for large industrial systems

FocusNet++: Attentive Aggregated Transformations for Efficient and Accurate Medical Image Segmentation

We propose a new residual block for convolutional neural networks and demonstrate its state-of-the-art performance in medical image segmentation. We combine attention mechanisms with group convolutions to create our group attention mechanism, which forms the fundamental building block of our network, FocusNet++. We employ a hybrid loss based on balanced cross entropy, Tversky loss and the adaptive logarithmic loss to enhance the performance along with fast convergence. Our results show that FocusNet++ achieves state-of-the-art results across various benchmark metrics for the ISIC 2018 melanoma segmentation and the cell nuclei segmentation datasets with fewer parameters and FLOPs.Comment: Published at ISBI 202

34th Midwest Symposium on Circuits and Systems-Final Program

Organized by the Naval Postgraduate School Monterey California. Cosponsored by the IEEE Circuits and Systems Society. Symposium Organizing Committee: General Chairman-Sherif Michael, Technical Program-Roberto Cristi, Publications-Michael Soderstrand, Special Sessions- Charles W. Therrien, Publicity: Jeffrey Burl, Finance: Ralph Hippenstiel, and Local Arrangements: Barbara Cristi