Similar Time Series Research Articles

유사 시계열 데이터 분석에 기반을 둔 교육기관의 전력 사용량 예측 기법

유사 시계열 데이터 분석에 기반을 둔 교육기관의 전력 사용량 예측 기법

Short-term wind power forecasts by a synthetical similar time series data mining method

As the aggravating influence of growing wind power, wind power forecasting research becomes more important in economic operation and safety management of power system. A novel short-term wind power forecasting methodology consists of a hybrid clustering method and a wavelet based neural network is introduced. The clustering similar measure function combines the Euclidean Distance and Angle Cosine together, aims to identify the similar wind speed days which are close in space distance and have similar variance trend synthetically. Then similar daily samples as the predicting days are treated as training samples of an improved particle swarm optimization based wavelet neural network. The proposed forecasting strategy is applied to two real wind farms in China. The results demonstrate that the strategy can identify the similar time series and improve the predicting accuracy effectively, compared with some other forecasting models.

A novel clustering-based method for time series motif discovery under time warping measure

The problem of time series motif discovery has attracted a lot of attention and is useful in many real-world applications. However, most of the proposed methods so far use Euclidean distance to deal with this problem. There has been one proposed method, called MDTW_WedgeTree, for time series motif discovery under DTW distance. But this method aims to deal with the case in which motif is the time series in a time series database which has the highest count of its similar time series within a range r. To adapt the above-mentioned method to the case in which motifs are frequently occurring subsequences of a longer time series, we modify MDTW_WedgeTree to a new algorithm for discovering “subsequence” motifs in time series under DTW. The proposed method consists of a segmentation method to divide the time series into motif candidates and a BIRCH-based clustering which can efficiently cluster motif candidate subsequences under DTW distance. Experimental results showed that our proposed method for discovering “subsequence” motifs performs very efficiently on large time series datasets while brings out high accuracy.

Downscaling GLDAS Soil Moisture Data in East Asia through Fusion of Multi-Sensors by Optimizing Modified Regression Trees

Soil moisture is a key part of Earth’s climate systems, including agricultural and hydrological cycles. Soil moisture data from satellite and numerical models is typically provided at a global scale with coarse spatial resolution, which is not enough for local and regional applications. In this study, a soil moisture downscaling model was developed using satellite-derived variables targeting Global Land Data Assimilation System (GLDAS) soil moisture as a reference dataset in East Asia based on the optimization of a modified regression tree. A total of six variables, Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced SCATterometer (ASCAT) soil moisture products, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and MODerate resolution Imaging Spectroradiometer (MODIS) products, including Land Surface Temperature, Normalized Difference Vegetation Index, and land cover, were used as input variables. The optimization was conducted through a pruning approach for operational use, and finally 59 rules were extracted based on root mean square errors (RMSEs) and correlation coefficients (r). The developed downscaling model showed a good modeling performance (r = 0.79, RMSE = 0.056 m3·m−3, and slope = 0.74). The 1 km downscaled soil moisture showed similar time series patterns with both GLDAS and ground soil moisture and good correlation with ground soil moisture (average r = 0.47, average RMSD = 0.038 m3·m−3) at 14 ground stations. The spatial distribution of 1 km downscaled soil moisture reflected seasonal and regional characteristics well, although the model did not result in good performance over a few areas such as Southern China due to very high cloud cover rates. The results of this study are expected to be helpful in operational use to monitor soil moisture throughout East Asia since the downscaling model produces daily high resolution (1 km) real time soil moisture with a low computational demand. This study yielded a promising result to operationally produce daily high resolution soil moisture data from multiple satellite sources, although there are yet several limitations. In future research, more variables including Global Precipitation Measurement (GPM) precipitation, Soil Moisture Active Passive (SMAP) soil moisture, and other vegetation indices will be integrated to improve the performance of the proposed soil moisture downscaling model.

Neural field model of seizure-like activity in isolated cortex.

Epileptiform discharges on an isolated cortex are explored using neural field theory. A neural field model of the isolated cortex is used that consists of three neural populations, excitatory, inhibitory, and excitatory bursting. Mechanisms by which an isolated cortex gives rise to seizure-like waveforms thought to underly pathological EEG waveforms on the deafferented cortex are explored. It is shown that the model reproduces similar time series and oscillatory frequencies for paroxysmal discharges when compared with physiological recordings both during acute and chronic deafferentation states. Furthermore, within our model ictal activity arises from perturbations to steady-states very close to the dynamical system's instability boundary; hence, these are distinct from corticothalamic seizures observed in the model for the intact brain which involved limit-cycle dynamics. The results are applied to experiments in deafferented cats.

Characterizing and attributing the warming trend in sea and land surface temperatures

Because of low-frequency internal variability, the observed and underlying warming trends in temperature series can be markedly different. Important differences in the observed nonlinear trends in hemispheric temperature series suggest that the northern and southern hemispheres have responded differently to the changes in the radiative forcing. Using recent econometric techniques, we can reconcile such differences and show that all sea and land temperatures share similar time series properties and a common underlying warming trend having a dominant anthropogenic origin. We also investigate the interhemispheric temperature asymmetry (ITA) and show that the differences in warming between hemispheres are in part driven by anthropogenic forcing but that most of the observed rapid changes is likely due to natural variability. The attribution of changes in ITA is relevant since increases in the temperature contrast between hemispheres could potentially produce a shift in the Intertropical Convergence Zone and alter rainfall patterns. The existence of a current slowdown in the warming and its causes are also investigated. The results suggest that the slowdown is a common feature in global and hemispheric sea and land temperatures that can, at least partly, be attributed to changes in anthropogenic forcing.

Similar Time Series Retrieval Using Only Important Segments

Similar time series searching plays an important role in applications such as time series classification and outlier detection. We observe that different segment of a time series may have different significance, thus propose to assign different weight to each segment, and extract those segments with highest weights for distance computation. Since these segments are more representative, we can achieve high accuracy of similarity search with much lower computation overhead. The result of experiments on both real world and synthetic data sets demonstrates that we can achieve comparable or even higher accuracy while largely reduce the computation overhead, if we use only those important segments rather than the whole time series while performing similarity search.

An Adaptive Density-Based Time Series Clustering Algorithm: A Case Study on Rainfall Patterns

Current time series clustering algorithms fail to effectively mine clustering distribution characteristics of time series data without sufficient prior knowledge. Furthermore, these algorithms fail to simultaneously consider the spatial attributes, non-spatial time series attribute values, and non-spatial time series attribute trends. This paper proposes an adaptive density-based time series clustering (DTSC) algorithm that simultaneously considers the three above-mentioned attributes to relieve these limitations. In this algorithm, the Delaunay triangulation is first utilized in combination with particle swarm optimization (PSO) to adaptively obtain objects with similar spatial attributes. An improved density-based clustering strategy is then adopted to detect clusters with similar non-spatial time series attribute values and time series attribute trends. The effectiveness and efficiency of the DTSC algorithm are validated by experiments on simulated datasets and real applications. The results indicate that the proposed DTSC algorithm effectively detects time series clusters with arbitrary shapes and similar attributes and densities while considering noises.

Stratified locality-sensitive hashing for accelerated physiological time series retrieval.

We introduce stratified locality-sensitive hashing (SLSH) for retrieving similar physiological waveform time series. SLSH further accelerates the sublinear retrieval time obtained by the standard locality-sensitive hashing (LSH) method. The standard family of locality-sensitive hash functions is limited to provide only a single perspective on the data due to its one-to-one relationship to a distinct distance function for measuring similarity. SLSH incorporates multiple locality-sensitive hash families with various distance functions enabling it to examine the data with more diverse and refined perspectives. We provide the procedures of SLSH with locality-sensitive hash families for the l1 and the cosine distances, and compare its performance to the standard LSH on an arterial blood pressure time series data extracted from the physiological waveform repository of the MIMIC2 database. The time to retrieve five most similar waveforms by SLSH is 14 times faster than the linear search and 1.7 times faster than the standard LSH when we allow 5% decrease in accuracy as a trade-off.

Varied pathways for simulated tropical cyclone rapid intensification. Part I: Precipitation and environment

Two 1‐km Weather Research and Forecasting model simulations of the rapid intensification (RI) periods of hurricanes Ike (2008) and Earl (2010), under low and high wind shear respectively, are performed to evaluate mechanisms linked to the initiation and maintenance of RI. Despite similar simulated intensification time series, the exhibited hydrometeor characteristics differ between the two cases since Ike possesses less asymmetry and less vigorous convection than Earl.An objective method to quantitatively estimate the radius of maximum wind (RMW) with respect to height is introduced for quantifying the primary circulation character of each storm, permitting analysis of where heating is most efficient for driving warm core growth and subsequent intensification. Following eye development in each, diabatic heating remains embedded within the RMW. Earl's RI onset coincides with an absolute maximum in diabatic heating occurring within the RMW, while Ike possesses relatively less diabatic heating inside the RMW throughout and lacks an absolute peak associated with RI. The majority of diabatic heating within the RMW of both cases occurs at subfreezing temperatures, indicative of the importance of clouds associated with ice processes in these RI simulations. Earl's diabatic heating peak is tied to a maximum in convective burst activity residing within the RMW. Earl's preference for more sparse but intense convection is associated with increased sea surface temperatures, surface heat fluxes, moisture convergence, and near‐surface equivalent potential temperature relative to the broader, weaker convective character in Ike.

Transcriptome dynamics of the microRNA inhibition response.

We report a high-resolution time series study of transcriptome dynamics following antimiR-mediated inhibition of miR-9 in a Hodgkin lymphoma cellline––the first such dynamic study of the microRNA inhibition response––revealing both general and specific aspects of the physiological response. We show miR-9 inhibition inducing a multiphasic transcriptome response, with a direct target perturbation before 4 h, earlier than previously reported, amplified by a downstream peak at ∼32 h consistent with an indirect response due to secondary coherent regulation. Predictive modelling indicates a major role for miR-9 in post-transcriptional control of RNA processing and RNA binding protein regulation. Cluster analysis identifies multiple co-regulated gene regulatory modules. Functionally, we observe a shift over time from mRNA processing at early time points to translation at later time points. We validate the key observations with independent time series qPCR and we experimentally validate key predicted miR-9 targets. Methodologically, we developed sensitive functional data analytic predictive methods to analyse the weak response inherent in microRNA inhibition experiments. The methods of this study will be applicable to similar high-resolution time series transcriptome analyses and provides the context for more accurate experimental design and interpretation of future microRNA inhibition studies.

Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia

Abstract Following extreme flooding in eastern Australia in 2011, the Australian Government established a programme to improve access to flood information across Australia. As part of this, a project was undertaken to map the extent of surface water across Australia using the multi-decadal archive of Landsat satellite imagery. A water detection algorithm was used based on a decision tree classifier, and a comparison methodology using a logistic regression. This approach provided an understanding of the confidence in the water observations. The results were used to map the presence of surface water across the entire continent from every observation of 27 years of satellite imagery. The Water Observation from Space (WOfS) product provides insight into the behaviour of surface water across Australia through time, demonstrating where water is persistent, such as in reservoirs, and where it is ephemeral, such as on floodplains during a flood. In addition the WOfS product is useful for studies of wetland extent, aquatic species behaviour, hydrological models, land surface process modelling and groundwater recharge. This paper describes the WOfS methodology and shows how similar time-series analyses of nationally significant environmental variables might be conducted at the continental scale.

Inverse density dependence of parity rates in the onchocerciasis vector Simulium damnosum s.l.

A correlation between parity rates and an index of adult numbers of Simulium damnosum s.l. (Diptera: Simuliidae) indicates an association, but does not prove causality or show the direction of any causal relationship. The question of whether adult numbers affect parity rates or vice versa is reminiscent of the age-old query of which of the chicken and the egg came first. A method for resolving such issues based on analyses of pairs of time series was proposed by Granger in 1969. When Granger's method was applied to monthly numbers of adult female S. damnosum s.l. caught attempting to bite humans at Asubende, Ghana, and their parity rates, a significant relationship (P = 0.005) emerged, clearly showing that parity rates were dependent on adult numbers. Implications of this inverse density dependence and the results of analyses of other similar time series are presented and discussed.

Using finite transducers for describing and synthesising structural time-series constraints

We describe a large family of constraints for structural time series by means of function composition. These constraints are on aggregations of features of patterns that occur in a time series, such as the number of its peaks, or the range of its steepest ascent. The patterns and features are usually linked to physical properties of the time series generator, which are important to capture in a constraint model of the system, i.e. a conjunction of constraints that produces similar time series. We formalise the patterns using finite transducers, whose output alphabet corresponds to semantic values that precisely describe the steps for identifying the occurrences of a pattern. Based on that description, we automatically synthesise automata with accumulators, as well as constraint checkers. The description scheme not only unifies the structure of the existing 30 time-series constraints in the Global Constraint Catalogue, but also leads to over 600 new constraints, with more than 100,000 lines of synthesised code.

Large-scale physiological waveform retrieval via locality-sensitive hashing.

We propose a fast, scalable locality-sensitive hashing method for the problem of retrieving similar physiological waveform time series. When compared to the naive k-nearest neighbor search, the method vastly speeds up the retrieval time of similar physiological waveforms without sacrificing significant accuracy. Our result shows that we can achieve 95% retrieval accuracy or better with up to an order of magnitude of speed-up. The extra time required in advance to create the optimal data structure is recovered when query quantity equals 15% of the repository, while the method incurs a trivial additional memory cost. We demonstrate the effectiveness of this method on an arterial blood pressure time series dataset extracted from the ICU physiological waveform repository of the MIMIC-II database.

Time-domain period detection in short-duration videos

This paper is concerned with detecting the period of cyclic object motion in a short video or sequence with a limited number of frames. This problem can be studied with either frequency-domain methods or time-domain methods. A frequency-domain method is fundamentally limited in terms of frequency resolution—especially with a small number of frames—and its ability to handle a periodic impulsive or spiky signal. Existing time-domain methods are primarily based on an analysis of the autocorrelation function of a signal and can be sensitive to noise in the signal. In this paper, we offer an alternative time-domain method. Rather than using autocorrelation as the basis, our proposed method uses peak analysis. Specifically, after computing the similarity between a reference image and those in the sequence, our algorithm applies one of two period detection procedures—one based on clustering and the other on watershed to analyze the peaks of the similarity time series—in estimating the period of object motion embedded in the similarity function. Video sequences from three different applications are used to establish the feasibility of our proposed algorithm and its superiority to competing algorithms.

Transcriptome dynamics of the microRNA inhibition response.

We report a high-resolution time series study of transcriptome dynamics following antimiR-mediated inhibition of miR-9 in a Hodgkin lymphoma cell-line—the first such dynamic study of the microRNA inhibition response—revealing both general and specific aspects of the physiological response. We show miR-9 inhibition inducing a multiphasic transcriptome response, with a direct target perturbation before 4 h, earlier than previously reported, amplified by a downstream peak at ∼32 h consistent with an indirect response due to secondary coherent regulation. Predictive modelling indicates a major role for miR-9 in post-transcriptional control of RNA processing and RNA binding protein regulation. Cluster analysis identifies multiple co-regulated gene regulatory modules. Functionally, we observe a shift over time from mRNA processing at early time points to translation at later time points. We validate the key observations with independent time series qPCR and we experimentally validate key predicted miR-9 targets. Methodologically, we developed sensitive functional data analytic predictive methods to analyse the weak response inherent in microRNA inhibition experiments. The methods of this study will be applicable to similar high-resolution time series transcriptome analyses and provides the context for more accurate experimental design and interpretation of future microRNA inhibition studies.

A multi-scale smoothing kernel for measuring time-series similarity

In this paper a kernel for time-series data is introduced so that it can be used for any data mining task that relies on a similarity or distance metric. The main idea of our kernel is that it should recognize as highly similar time-series that are essentially the same but may be slightly perturbed from each other: for example, if one series is shifted with respect to the other or if it slightly misaligned. Namely, our kernel tries to focus on the shape of the time-series and ignores small perturbations such as misalignments or shifts. First, a recursive formulation of the kernel directly based on its definition is proposed. Then it is shown how to efficiently compute the kernel using an equivalent matrix-based formulation. To validate the proposed kernel three experiments have been carried out. As an initial step, several synthetic datasets have been generated from UCR time-series repository and the KDD challenge of 2007 with the purpose of validating the kernel-derived distance over shifted time-series. Also, the kernel has been applied to the original UCR time-series to analyze its potential in time-series classification in conjunction with Support Vector Machines. Finally, two real-world applications related to ozone concentration in atmosphere and electricity demand have been considered.

A Time Series Clustering Technique based on Community Detection in Networks

Abstract Time series clustering is a research topic of practical importance in temporal data mining. The goal is to identify groups of similar time series in a data base. In this paper, we propose a technique for time series clustering via community detection in complex networks. First, we construct a network where every vertex represents a time series connected its most similar ones,. Similarity was calculated using different time series distance functions. Then, we applied a community detection algorithm to identify groups of strongly connected vertices in order to produce time series clusters. We verified which distance function works better with every clustering algorithm and compared them to our approach. The experimental results show that our approach statistically outperformed many traditional clustering algorithms. We find that the community detection approach can detect groups that other techniques fail to identify.

Improved precision of delay times determined through cross correlation achieved by out-member averaging

The estimation of differential time delays between similar but noisy time series by cross-correlation is an important component of many data processing applications. In many cases, delays are determined for every pair of time series drawn from a large set of them. For instance, modern earthquake location techniques utilize the differential delays between all P waves (or S waves) observed at a common station, sets that could have thousands of members (one for each earthquake pair). We show that the variance of delay time estimates can be reduced by a factor of 2 to 4 through a process we term out-member averaging. It utilizes an estimate of the delay time constructed by adding together the delays between each member of the pair and a third time series. In addition to decreasing the variance of the estimates, thus potentially improving the accuracy of earthquake locations by a factor of 2–4, it also demonstrates that data from an earthquake can contribute to the location process, even when insufficient data are available to locate that particular earthquake.