Dynamic Time Warping Method Research Articles

Weighted dynamic time warping for traffic flow clustering

This paper presents a novel similarity measure to identify interesting traffic patterns on a large traffic flow time series data for the central suburbs of Melbourne city in Australia. This new measure is a weighted Dynamic Time Warping (DTW) method based on Gaussian probability function, named GWDTW, that reflects the relative importance of peak hours. We have shown its superior performance over two benchmark similarity measures, the Euclidean distance and conventional DTW measure, on the intersection clustering task using k-medoids clustering algorithm, with respect to both internal and external evaluation measures. With intensive evaluation, the results show that GWDTW is a very effective similarity measure for modelling traffic behaviours, which can provide policy makers with more valuable information for infrastructure design, and smart city development.

Implementation and evaluation of a multivariate abstraction-based, interval-based dynamic time-warping method as a similarity measure for longitudinal medical records

We extended dynamic time warping (DTW) into interval-based dynamic time warping (iDTW), including (A) interval-based representation (iRep): [1] abstracting raw, time-stamped data into interval-based abstractions, [2] comparison-period scoping, [3] partitioning abstract intervals into a given temporal granularity; (B) interval-based matching (iMatch): matching partitioned, abstract-concepts records, using a modified DTW. Using domain knowledge, we abstracted the raw data of medical records, for up to three concepts out of four or five relevant concepts, into two interval types: State abstractions (e.g. LOW, HIGH) and Gradient abstractions (e.g. INCREASING, DECREASING). We created all uni-dimensional (State or Gradient) or multi-dimensional (State and Gradient) abstraction combinations. Tasks: Classifying 161 oncology patients records as autologous or allogenic bone-marrow transplantation; classifying 125 hepatitis patients records as B or C hepatitis; predicting micro- or macro-albuminuria in the next year for 151 Type 2 diabetes patients. We used a k-Nearest-Neighbors majority, k = an odd number from 1 to SQRT(N), N = set size. 75,936 10-fold cross-validation experiments were performed: 33,600 (Oncology), 28,800 (Hepatitis), 13,536 (Diabetes). Measures: Area Under the Curve (AUC), optimal Youden's Index. Paired t-tests compared result vectors for equivalent configurations other than a tested variable, to determine a significant mean accuracy difference (P<0.05). Mean classification and prediction using abstractions was significantly better than using only raw time-stamped data. In each domain, at least one abstraction combination led to a significantly better mean performance than raw data. Increasing feature number and using Multi-dimensional abstractions enhanced performance. Unlike when using raw data, optimal mean performance was often reached with k=5, using abstractions.

The use of the dynamic time warping (DTW) method to describe the COVID-19 dynamics in Poland

Research background: In recent times, the whole world has been severely affected by the COVID-19 pandemic. The influence of the epidemic on the society and the economy has caused a great deal of scientific interest. The development of the pandemic in many countries was analyzed using various models. However, the literature on the dissemination of COVID-19 lacks econometric analyzes of the development of this epidemic in Polish voivodeships.&#x0D; Purpose of the article: The aim of the study is to find similarities in time series for infected with and those who died of COVID-19 in Polish voivodeships using the method of dynamic time warping.&#x0D; Methods: The dynamic time warping method allows to calculate the distance between two time series of different lengths. This feature of the method is very important in our analysis because the coronavirus epidemic did not start in all voivodeships at the same time. The dynamic time warping also enables an adjustment of the timeline to find similar, but shifted, phases. Using this method, we jointly analyze the number of infected and deceased people in each province. In the next step, based on the measured similarity of the time series, the voivodeships are grouped hierarchically.&#x0D; Findings &amp; value added: We use the dynamic time warping to identify groups of voivodeships affected by the epidemic to a different extent. The classification performed may be useful as it indicates patterns of the COVID-19 disease evolution in Polish voivodeships. The results obtained at the regional level will allow better prediction of future infections. Decision makers should formulate further recommendations for lockdowns at the local level, and in the long run, adjust the medical infrastructure in the regions accordingly. Policymakers in other countries can benefit from the findings by shaping their own regional policies accordingly.

Let's do the time warp again: non‐linear time series matching as a tool for sequentially structured data in ecology

AbstractEcological patterns are often fundamentally chronological. However, generalization of data is necessarily accompanied by a loss of detail or resolution. Temporal data in particular contain information not only in data values but in the temporal structure, which is lost when these values are aggregated to provide point estimates. Dynamic time warping (DTW) is a time series comparison method that is capable of efficiently comparing series despite temporal offsets that confound other methods. The DTW method is both efficient and remarkably flexible, capable of efficiently matching not only time series but also any sequentially structured data set, which has made it a popular technique in machine learning, artificial intelligence, and big data analytical tasks. DTW is rarely used in ecology despite the ubiquity of temporally structured data. As technological advances have increased the richness of small‐scale ecological data, DTW may be an attractive analysis technique because it is able to utilize the additional information contained in the temporal structure of many ecological data sets. In this study, we use an example data set of high‐resolution fish movement records obtained from otolith microchemistry to compare traditional analysis techniques with DTW clustering. Our results suggest that DTW is capable of detecting subtle behavioral patterns within otolith data sets which traditional data aggregation techniques cannot. These results provide evidence that the DTW method may be useful across many of the temporal data types commonly collected in ecology, as well other sequentially ordered “pseudo time series” data such as classification of species by shape.

The Connections between COVID-19 and the Energy Commodities Prices: Evidence through the Dynamic Time Warping Method

The main objective of the study is to assess the similarity between the time series of energy commodity prices and the time series of daily COVID-19 cases. The COVID-19 pandemic affects all aspects of the global economy. Although this impact is multifaceted, we assess the connections between the number of COVID-19 cases and the energy commodities sector. We analyse these connections by using the Dynamic Time Warping (DTW) method. On this basis, we calculate the similarity measure—the DTW distance between the time series—and use it to group the energy commodities according to their price change. Our analysis also includes finding the time shifts between daily COVID-19 cases and commodity prices in subperiods according to the chronology of the COVID-19 pandemic. Our findings are that commodities such as ULSD, heating oil, crude oil, and gasoline are weakly associated with COVID-19. On the other hand, natural gas, palm oil, CO2 allowances, and ethanol are strongly associated with the development of the pandemic.

Gait Symmetry Analysis Based on Dynamic Time Warping

Background: Gait deviations may negatively affect the articular surfaces of the lower extremity joints and lead to some minor degenerative changes. The simplest method for gait evaluation is to assess the symmetry of its parameters, assuming that each undisturbed gait should be symmetrical. This study aims to quantify the degree of asymmetry of kinematic and kinetic parameters caused by the presence of different ankle orthosis settings using Dynamic Time Warping (DTW). Methods: Barefoot gait and gait with four different walker settings were investigated in eighteen healthy persons. Kinematic and kinetic parameters were measured using the Vicon system and Kistler plates. Symmetry was assessed using the DTW method. Results: It was shown that the presence of different ankle orthosis settings significantly disturbs the symmetry of all lower limb kinematic parameters and only knee and hip torques. The highest values of asymmetry were noted for the walker set at 15° of dorsiflexion (15DF). Conclusions: The DTW method allowed us to quantify the degree of asymmetry throughout the gait cycle in relation to barefoot walking. Our results suggest that each orthosis position analysed in this study fulfills its protective function, but gait 15DF can lead to the overload of knee and hip joints.

Mobility Pattern Changes in Indonesia in Response to COVID-19

All countries affected by the COVID-19 pandemic have established several policies to control the spread of the disease. The government of Indonesia has enforced a work-from-home policy and large-scale social restrictions in most regions that result in the changes in community mobility in various categories of places. This study aims to (1) investigate the impact of large-scale restrictions on provincial-level mobility in Indonesia, (2) categorize provinces based on mobility patterns, and (3) investigate regional socio-economic characteristics that may lead to different mobility patterns. This study utilized Provincial-level Google Mobility Index, Flight data scraped from daily web, and regional characteristics (e.g., poverty rate, percentages of informal workers). A Dynamic Time Warping method was employed to investigate the clusters of mobility. The study shows an intense trade-off of mobility pattern between residential areas and public areas. In general, during the first 2.5 months of the pandemic, people had reduced their activities in public areas and preferred to stay at home. Meanwhile, provinces have different mobility patterns from each other during the period of the large-scale restrictions. The differences in mobility are mainly led by the percentage of formal workers in each region.

Modeling detour decision combined the tactical and operational layer based on perceived density

A microscopic model combining Voronoi-based detour SFM of the operational level and vision-based path decisions of the tactical level is developed in order to improve pedestrian detour behavior simulation at the operational level. The model is used to simulate the detour behavior of pedestrians in the circle antipode experiment. By modeling avoid crowd decision process based on visual perception, and take the path direction as the target direction of the Voronoi-based detour SFM, this model can well describe the detour behavior of the circle antipode experiment. The central area density, the mean local density, the average speed time series and the travel time, detour level, route length distribution are chosen as indexes, and then the Kolmogorov–Smirnov (K–S) test and the Dynamic Time Warping (DTW) method are applied to evaluate these indexes of the traditional SFM, Voronoi-based detour SFM and this Double-Layer Decision-Making model. Results show that the indexes of the Double-Layer pedestrian simulation model are superior to those of other models and significantly superior at the detour level, the travel time, and the route length.

Bedding Angle Identification from BIF Marker Shales via Modified Dynamic Time Warping

When modelling a stratified orebody, accurately representing the dip and dip direction is important for accurate resource estimation. In the banded iron formation-hosted iron ore deposits in the Pilbara region of Western Australia, these quantities can be determined using marker shales from nearby holes. These marker shales are identified using natural gamma logs and are generally manually processed. Therefore, an automated method for matching natural gamma logs between holes is desirable. Dynamic time warping (DTW) can match two signals where there is stretching or distortion. This study presents a modified, iterative version of DTW for matching downhole natural gamma logs. This new method accounts for large differences in length of the two signals by comparing different segments of the signals. Several metrics were then used to rank potential matches between signals. The proposed iterative DTW method had an accuracy of 90%, compared with 67% for the standard DTW. Once matched, signals can be used to estimate the bedding angle at each hole. A point in one hole was matched to as many nearby holes as possible, creating a set of points located on the same surface. A localized plane was then fitted to these points. These bedding angles were used to reconstruct a surface representing the bedding. While the signal matching was accurate, the sparsity of correctly matched holes limits the accuracy of the calculated surface. Even with sparse gradient fields, a reasonable approximation of the bedding could be achieved.

Time-weighted dynamic time warping analysis for mapping interannual cropping practices changes in large-scale agro-industrial farms in Brazilian Cerrado

Methods for crop phenology detection using time series analysis have provided accurate information for large agricultural areas in shorter processing times, which can be useful for agronomic management and supply chain monitoring. Given the crop dynamics in the Brazilian Cerrado, with alternating crop type plantings, crop successions, and crop rotations, as well as climate and crop practices variation between harvest periods, these methods can be useful for detecting subtle land use and land cover changes at farm and crop field scales, improving thematic classifications and the near real-time crop monitoring. In this study, the Time-Weighted Dynamic Time Warping method was applied to recognize patterns in Moderate Resolution Imaging Spectroradiometer (MODIS) time series for land use and cover classification, identifying crop successions and rotations at crop field level in a large-scale agro-industrial agglomerate of farms located at Brazilian Cerrado. We detected and analyzed temporal cropping patterns in training samples to classify the MODIS time series and images, using a robust ground truth data set for validation. The method distinguished Cotton-fallow, Soybean-cotton, Soybean-maize, and Soybean-millet cropping patterns with an overall accuracy above 85% for all evaluated harvest periods. Seasonal variations in the crop fields, caused by interannual succession and rotation, were detected. The method demonstrated the benefit of creating a spatial vector data set for supporting decision-making in several crop management contexts, improving crop and supply chain monitoring.

Perbandingan Kualitas Suara Smartphone Menggunakan Metode Dynamic Time Warping (DTW)

Smartphones are telecommunication devices that play a significant role in daily life. The sound quality produced by a smartphone becomes important for users, considering that poor sound quality might cause misunderstandings in communication. This study provides an illustration of the application of Dynamic Time Warping (DTW) in the comparison of the sound quality produced by a smartphone. In addition to the DTW, the median test and its confidence interval are also used to determine the sound quality of a smartphone. The data employed are primary data in the form of voice recordings of six people that saying five sample sentences, each of which is repeated five times through four different smartphone types that are used as examples. So that the total voice recordings for each smartphone are 150 pieces. This study aims to compare the sound quality produced by those smartphones. The results of this study indicate that although smartphones type 2, 3 and 4 have similar sound quality, the sound quality produced by smartphones type 4 is more stable than other types. Therefore, this study concludes the smartphone type 4 is the smartphone with the most satisfying sound quality. Furthermore, this study showed that the DTW method is effective in analyzing the sound quality of a smartphone.&#x0D;

Implementation of audio recognition using mel frequency cepstrum coefficient and dynamic time warping in wirama praharsini

Sekar Agung or wirama is a Balinese classic work which contains of moral values, and is usually sung during traditional or religious ceremonies. The classic nature of wirama made this art was abandoned by the younger generation who were less interested to learn or preserve it. Related to the problem, this study aimed at conducting sound matching as a medium to learn wirama praharsini based on the rule of guru and laghu. Wirama Praharsini was chosen because of the simple way of singing and pronunciation when it was compared to the other kinds of wirama. This study applied voice recognition method to identify words’ pronunciation in wirama praharsini. MFCC (Mel Frequency Cepstrum Coefficient) was used to extract the voice feature from expert and the tester. The extraction results were compared with the DTW (Dynamic Time Warping) method that was used to compare two sound features from MFCC process. The result of this study showed average accuracy value of 88.89% which indicated that the implementation of MFCC and DTW method could be done to recognize the wirama praharsini pronunciation and detect the right way to sing it based on the rules of guru and laghu in wirama.

Price Stability of Cryptocurrencies as a Medium of Exchange

We present positive evidence of price stability of cryptocurrencies as a medium of exchange. For the sample years from 2016 to 2020, the prices of major cryptocurrencies are found to be stable, relative to major financial assets. Specifically, after filtering out the less-than-one-month cycles, we investigate the daily returns in US dollars of the major cryptocurrencies (i.e., Bitcoin, Ethereum, and Ripple) as well as their comparators (i.e., major legal tenders, the Euro and Japanese yen, and the major stock indexes, S&P 500 and MSCI World Index). We examine the stability of the filtered daily returns using three different measures. First, the Pearson correlations increased in later years in our sample. Second, based on the dynamic time-warping method that allows lags and leads in relations, the similarities in the daily returns of cryptocurrencies with their comparators have been present even since 2016. Third, we check whether the cumulative sum of errors to predict cryptocurrency prices, assuming stable relations with comparators' daily returns, does not exceeds the bounds implied by the Black-Scholes model. This test, in other words, does not reject the efficient market hypothesis.

Iqra reading verification with mel frequency cepstrum coefficient and dynamic time warping

Al-Quran is a holy book of guidance from Allah SWT that was revealed through Prophet Muhammad SAW for Muslims. Muslims should learn to read Al-Quran properly and correctly. One of the methods in learning the Al-Quran is the Iqra method. Iqra method is learning in reading and writing the Al-Quran by using Iqra books delivered classically and individually. Learning methods will continue to be developed to adapt to current conditions. As technology develops today, the method of learning the Al-Quran has also developed, one of which is using sound. In this research, the writer will create a system that verifies the sound of reading the Iqra which is one of the basic methods in learning to read the Al-Quran. The system is designed using the Mel Frequency Cepstrum Coefficient (MFCC) method as a voice feature extension and the Dynamic Time Warping (DTW) method for matching the results of the voice features. In this study, the best accuracy in the Iqra reading sound was found to be 82% with 13 MFCC coefficients.

Simplified Automatic Seismic to Well Tying Using Smooth Dynamic Time Warping Technique in R

Seismic well tying is a crucial part of the interpretation phase in exploration seismology. Tying wells usually involves forward modeling a synthetic seismogram from sonic and density logs and then matching the obtained synthetic seismogram to the seismic reflection data. A huge amount of time is required to deal with it, yet the outcome signal may not be satisfying and may be suffering a low cross correlation between the seismic signal and the synthetic one; it also requires a high quality synthetic trace. Another problem with the so-called manual tying is that the tying process is not repeatable, indicating that one can rarely obtain the same stretched and squeezed signal if the tying procedure is repeated. In recent years, some researchers have used the dynamic time warping (DTW) method to address well tying problems. They have obtained good results according to the correlation between the seismic signal and the warped synthetic signal. This research demonstrates that the result will be better if filtering is applied before tying, and then the warped signal is smoothed. We also propose a simpler algorithm for extracting a warped signal from the warping curve and the original synthetic trace, which gives rise to better performance for well tying.

A Fused Method of Machine Learning and Dynamic Time Warping for Road Anomalies Detection

To discover the condition of roads, a large number of detection algorithms have been proposed, most of which apply machine learning methods by time and frequency processing in acceleration and velocity data. However, few of them pay attention to the similarity of the data itself when the vehicle passes over the road anomalies. In this article, we propose a method to detect road anomalies by comparing the data windows with various length using Dynamic Time Warping(DTW) method. We propose a model to prove that the maximum acceleration of a vehicle passing through a road anomaly is linear with the height of the road barrier, and it&#x2019;s verified by an experiment. This finding suggests that it is reasonable to divide the window by threshold detection. We also apply a brief random forest filter to roughly distinguish normal windows from anomaly windows using the aforementioned theory, in order to reduce the time consumption. From our study, a system is proposed that utilizes a series of acceleration data to discover where might be anomalies on the road, named as Quick Filter Based Dynamic Time Warping (QFB-DTW). We show that our method performs clearly beyond some existing methods. To support this conclusion, experiments are conducted based on three data sets and the results are statistically analyzed. We expect to lay the first step to some new thoughts to the field of road anomalies detection in subsequent work.

Determining grasp selection from arm trajectories via deep learning to enable functional hand movement in tetraplegia

BackgroundCervical spinal cord injury severely affects grasping ability of its survivors. Fortunately, many individuals with tetraplegia retain residual arm movements that allow them to reach for objects. We propose a wearable technology that utilizes arm movement trajectory information and deep learning methods to determine grasp selection. Furthermore, we combined this approach with neuromuscular stimulation to determine if self-driven functional hand movement could be enabled in spinal cord injury participants.MethodsTwo cervical SCI participants performed arbitrary and natural reaching movements toward target objects in three-dimensional space, which were recorded using an inertial sensor worn on their wrist. Time series classifiers were trained to recognize the trajectories using either a Dynamic Time Warping (DTW) algorithm or a Long Short-Term Memory (LSTM) recurrent neural network. As an initial proof-of-concept, we demonstrate real-time classification of the arbitrary movements using DTW only (due to its implementation simplicity), which when used in combination with a high density neuromuscular stimulation sleeve with textile electrodes, enabled participants to perform functional grasping.ResultsParticipants were able to consistently perform arbitrary two-dimensional and three-dimensional arm movements which could be classified with high accuracy. Furthermore, it was found that natural reaching trajectories for two different target objects (requiring two different grasp types) were distinct and also discriminable with high accuracy. In offline comparisons, LSTM (mean accuracies 99%) performed significantly better than DTW (mean accuracies 86 and 83%) for both arbitrary and natural reaching movements, respectively. Type I and II errors occurred more frequently for DTW (up to 60 and 15%, respectively), whereas it stayed under 5% for LSTM. Also, DTW achieved online accuracy of 79%.ConclusionsWe demonstrate the feasibility of utilizing arm trajectory information to determine grasp selection using a wearable inertial sensor along with DTW and deep learning methods. Importantly, this technology can be successfully used to control neuromuscular stimulation and restore functional independence to individuals living with paralysis.Trial registrationNCT, NCT03385005. Registered September 26, 2017

A Hybrid Landslide Displacement Prediction Method Based on CEEMD and DTW-ACO-SVR-Cases Studied in the Three Gorges Reservoir Area.

Accurately predicting the surface displacement of the landslide is important and necessary. However, most of the existing research has ignored the frequency component of inducing factors and how it affects the landslide deformation. Therefore, a hybrid displacement prediction model based on time series theory and various intelligent algorithms was proposed in this paper to study the effect of frequency components. Firstly, the monitoring displacement of landslide from the Three Gorges Reservoir area (TGRA) was decomposed into the trend and periodic components by complete ensemble empirical mode decomposition (CEEMD). The trend component can be predicted by the least square method. Then, time series of inducing factors like rainfall and reservoir level was reconstructed into high frequency components and low frequency components with CEEMD and t-test, respectively. The dominant factors were selected by the method of dynamic time warping (DTW) from the frequency components and other common factors (e.g., current monthly rainfall). Finally, the ant colony optimization-based support vector machine regression (ACO-SVR) is utilized for prediction purposes in the TGRA. The results demonstrate that after considering the frequency components of landslide-induced factors, the accuracy of the displacement prediction model based on ACO-SVR is better than that of other models based on SVR and GA-SVR.

Continuous Focal Group Follows Operated by a Drone Enable Analysis of the Relation Between Sociality and Position in a Group of Male Risso’s Dolphins (Grampus griseus)

Relationships between social status and relative position of group-living animals have been described in a variety of species. For wild cetaceans, who spend most of their time underwater, collecting detailed, continuous data to assess such relationships depends highly on group size, formation, shyness of animals and observation platform. We test a new method for focal group sampling using an Unmanned Aerial Vehicle (UAV), focusing on one long-term followed group of 13 male Risso’s dolphins (Grampus griseus) in the Azores, Portugal. We aim to assess the usefulness of a UAV in delivering robust data to evaluate sociality in relation to relative position. Our analysis is based on recordings of synchronous breathing events, which are taken as an indicator of association strength. Twenty-one separate UAV flights were performed during seven surveys in July-August 2017, recording 2,886 breathing events and 571 synchronous dyads. Results showed strong differences in sociality between individuals and identified two strongly associated pairs, one strongly associated trio and six less associated individuals within the group. We subsequently created continuous time series of relative positions by interpolation of the positions recorded with the UAV at breathing events, and applied the Dynamic Time Warping method to assess associations based on relative position. This analysis identified more detailed association patterns than the synchrony analysis, and revealed a correlation between measures of sociality and relative position, at an individual and sub-cluster level, which may indicate dominant relationships. We compared results with those obtained with Photo-ID to assess any observation bias related to using a UAV. We found that 37% more breathing events were recorded with the UAV, and 21% more synchronous dyads detected, compared with Photo-ID, collected over the same observation periods, but, based on synchrony data, both methods yielded very similar results. We conclude that using a UAV for focal group follows of Risso’s dolphins enables a more granular study of association patterns than Photo-ID, by taking into account the relative position of individuals. The correlation found between measures of sociality and relative position holds promise for using UAVs in future studies of dominant relationships in Risso’s dolphins and other cetacean species.

A new higher-resolution multi-trace seismic discontinuity attribute based on a Dynamic Time Warping algorithm

Abstract A new machine-learning approach based on a Dynamic Time Warping (DTW) algorithm is introduced to detect faults and fractures in 3D seismic data from an unconventional resource play in Eastern Cis- Caucasia (Russia). This novel approach allows for better edge detection in seismic amplitude volumes because it employs a detailed comparison of two neighbouring traces to detect discontinuity via a minimal horizontal distance. For benchmarking purposes the proposed DTW method is compared to a widely used multi-trace attribute (Variance). Subsequently, both calculated attribute cubes serve as an input for ANT-Tracking to delineate fault strike and fracture corridor trends. A comparison of results shows that better resolution and more complete fault images are obtained when the DTW method is applied.