KV-match: A Subsequence Matching Approach Supporting Normalization and Time Warping [Extended Version]

The volume of time series data has exploded due to the popularity of new applications, such as data center management and IoT. Subsequence matching is a fundamental task in mining time series data. All index-based approaches only consider raw subsequence matching (RSM) and do not support subsequence normalization. UCR Suite can deal with normalized subsequence match problem (NSM), but it needs to scan full time series. In this paper, we propose a novel problem, named constrained normalized subsequence matching problem (cNSM), which adds some constraints to NSM problem. The cNSM problem provides a knob to flexibly control the degree of offset shifting and amplitude scaling, which enables users to build the index to process the query. We propose a new index structure, KV-index, and the matching algorithm, KV-match. With a single index, our approach can support both RSM and cNSM problems under either ED or DTW distance. KV-index is a key-value structure, which can be easily implemented on local files or HBase tables. To support the query of arbitrary lengths, we extend KV-match to KV-match$_{DP}$, which utilizes multiple varied-length indexes to process the query. We conduct extensive experiments on synthetic and real-world datasets. The results verify the effectiveness and efficiency of our approach.Comment: 13 page

Periodicities of FX Markets in Intrinsic Time

This paper utilises advanced methods from Fourier Analysis in order to describe financial ultra-high frequent transaction data. The Lomb-Scargle Fourier Transform is used to take into account the irregularity in spacing in the time-domain. It provides a natural framework for the power spectra of different inhomogeneous time series processes to be easily and quickly estimated,without significant computational effort, in contrast to the common econometric approaches in the finance literature. An event-based approach (intrinsic time), which by its own nature is inhomogeneous in time, is employed using different event thresholds to filter the foreign exchange tick-data leading to a power-law relationship. The calculated spectral density demonstrates that the price process in intrinsic time contains different periodic components, especially in the medium-long term, implying the existence of new stylised facts of ultra-high frequency data in the frequency domain

Accelerating incoherent dedispersion

Incoherent dedispersion is a computationally intensive problem that appears frequently in pulsar and transient astronomy. For current and future transient pipelines, dedispersion can dominate the total execution time, meaning its computational speed acts as a constraint on the quality and quantity of science results. It is thus critical that the algorithm be able to take advantage of trends in commodity computing hardware. With this goal in mind, we present analysis of the 'direct', 'tree' and 'sub-band' dedispersion algorithms with respect to their potential for efficient execution on modern graphics processing units (GPUs). We find all three to be excellent candidates, and proceed to describe implementations in C for CUDA using insight gained from the analysis. Using recent CPU and GPU hardware, the transition to the GPU provides a speed-up of 9x for the direct algorithm when compared to an optimised quad-core CPU code. For realistic recent survey parameters, these speeds are high enough that further optimisation is unnecessary to achieve real-time processing. Where further speed-ups are desirable, we find that the tree and sub-band algorithms are able to provide 3-7x better performance at the cost of certain smearing, memory consumption and development time trade-offs. We finish with a discussion of the implications of these results for future transient surveys. Our GPU dedispersion code is publicly available as a C library at: http://dedisp.googlecode.com/Comment: 15 pages, 4 figures, 2 tables, accepted for publication in MNRA