A Novel Framework for Online Amnesic Trajectory Compression in Resource-constrained Environments

State-of-the-art trajectory compression methods usually involve high space-time complexity or yield unsatisfactory compression rates, leading to rapid exhaustion of memory, computation, storage and energy resources. Their ability is commonly limited when operating in a resource-constrained environment especially when the data volume (even when compressed) far exceeds the storage limit. Hence we propose a novel online framework for error-bounded trajectory compression and ageing called the Amnesic Bounded Quadrant System (ABQS), whose core is the Bounded Quadrant System (BQS) algorithm family that includes a normal version (BQS), Fast version (FBQS), and a Progressive version (PBQS). ABQS intelligently manages a given storage and compresses the trajectories with different error tolerances subject to their ages. In the experiments, we conduct comprehensive evaluations for the BQS algorithm family and the ABQS framework. Using empirical GPS traces from flying foxes and cars, and synthetic data from simulation, we demonstrate the effectiveness of the standalone BQS algorithms in significantly reducing the time and space complexity of trajectory compression, while greatly improving the compression rates of the state-of-the-art algorithms (up to 45%). We also show that the operational time of the target resource-constrained hardware platform can be prolonged by up to 41%. We then verify that with ABQS, given data volumes that are far greater than storage space, ABQS is able to achieve 15 to 400 times smaller errors than the baselines. We also show that the algorithm is robust to extreme trajectory shapes.Comment: arXiv admin note: substantial text overlap with arXiv:1412.032

An Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation

Monotonicity is a simple yet significant qualitative characteristic. We consider the problem of segmenting a sequence in up to K segments. We want segments to be as monotonic as possible and to alternate signs. We propose a quality metric for this problem using the l_inf norm, and we present an optimal linear time algorithm based on novel formalism. Moreover, given a precomputation in time O(n log n) consisting of a labeling of all extrema, we compute any optimal segmentation in constant time. We compare experimentally its performance to two piecewise linear segmentation heuristics (top-down and bottom-up). We show that our algorithm is faster and more accurate. Applications include pattern recognition and qualitative modeling.Comment: This is the extended version of our ICDM'05 paper (arXiv:cs/0702142

Monotone Pieces Analysis for Qualitative Modeling

It is a crucial task to build qualitative models of industrial applications for model-based diagnosis. A Model Abstraction procedure is designed to automatically transform a quantitative model into qualitative model. If the data is monotone, the behavior can be easily abstracted using the corners of the bounding rectangle. Hence, many existing model abstraction approaches rely on monotonicity. But it is not a trivial problem to robustly detect monotone pieces from scattered data obtained by numerical simulation or experiments. This paper introduces an approach based on scale-dependent monotonicity: the notion that monotonicity can be defined relative to a scale. Real-valued functions defined on a finite set of reals e.g. simulation results, can be partitioned into quasi-monotone segments. The end points for the monotone segments are used as the initial set of landmarks for qualitative model abstraction. The qualitative model abstraction works as an iteratively refining process starting from the initial landmarks. The monotonicity analysis presented here can be used in constructing many other kinds of qualitative models; it is robust and computationally efficient

Застосування методів нелінійного аналізу для побудови системи моніторінгу фондових ринків

Метою даної роботи є вирішення проблеми згладжування міри ламінарність рекурентного кількісного аналізу для побудови системи моніторингу фондових ринків

A Better Alternative to Piecewise Linear Time Series Segmentation

Time series are difficult to monitor, summarize and predict. Segmentation organizes time series into few intervals having uniform characteristics (flatness, linearity, modality, monotonicity and so on). For scalability, we require fast linear time algorithms. The popular piecewise linear model can determine where the data goes up or down and at what rate. Unfortunately, when the data does not follow a linear model, the computation of the local slope creates overfitting. We propose an adaptive time series model where the polynomial degree of each interval vary (constant, linear and so on). Given a number of regressors, the cost of each interval is its polynomial degree: constant intervals cost 1 regressor, linear intervals cost 2 regressors, and so on. Our goal is to minimize the Euclidean (l_2) error for a given model complexity. Experimentally, we investigate the model where intervals can be either constant or linear. Over synthetic random walks, historical stock market prices, and electrocardiograms, the adaptive model provides a more accurate segmentation than the piecewise linear model without increasing the cross-validation error or the running time, while providing a richer vocabulary to applications. Implementation issues, such as numerical stability and real-world performance, are discussed.Comment: to appear in SIAM Data Mining 200

Прогнозирование существенно нестационарных многофакторных временных рядов на примере показателя инвестиций российских небанковских корпораций за рубеж

В данной работе предпринимается попытка разработки алгоритма, позволяющего прогнозировать будущие значения макроэкономических показателей, принимая во внимание нестационарность процессов при изменении структуры модели на примере показателя объема инвестиций российских небанковских корпораций за рубеж

Адаптивная обработка нестационарных временных рядов на основе нечеткого подхода

На основе объединения нечеткого пакетного способа обработки и сегментации временных рядов с рекуррентными процедурами обработки текущих значений, предложен онлайн метод сегментации многомерных временных рядов, который применим для обнаружения однородных сегментов в реальном режиме времени на основе потоковых данныхBy combining the fuzzy batch mode processing and segmentation of time series with recurrent processing procedures current values offered online segmentation method of multivariate time series, which is useful for the detection of homogeneous segments in real time based on the data strea

Разработка категорной модели реализации жизненого цикла процесса выбора мероприятий

Рассматриваются методы формирования категорных моделей сложных объектов. В категорно-функторном представлении формализованы показатели эффективности, бизнес-процессы и мероприятия, позволяющие формализовать формирование списка мероприятий для оперативного управления БПThe methods of forming categorical models of complex objects is considered. In the representation of category and functor the performance of indicators, business processes and activities are formalized This make it possible to formalize the generation of a list of activities for the operational management of business processe

Smoothening and Segmentation of ECG Signals Using Total Variation Denoising –Minimization-Majorization and Bottom-Up Approach

AbstractAn ECG Signal records electrical activity of heart. It includes information on heart's rhythm and is useful for diagnosis of heart related diseases. It encounters with various artifacts during acquisition and transmission. The unwanted signals/noises present in ECG signals disturb the clinical information present in it. This paper tries to reduce unwanted signals through Majorization-Minorization approach to optimize total variation in the signals. The denoised signal is then segmented using bottom up approach. The results show significant improvement in signal to noise ratio and successful segmentation of sections of ECG signals