Cluster Analysis Of Time Series Research Articles

Differentiation of countries in terms of the shore of domestic value added in exports

The emergence of global value chains entails that measuring the benefits of foreign trade cannot be limited to measuring trade flows nor their structure. The article aims atpresentingthe results of the time series cluster analysis of the share of the domestic value added of gross exports. It isbased ondata from the latest TiVA database (Trade in Value Added) from December 2018,covering the period 2005–2016. Four clusters of countries were identified. The cluster containing countries with the highest values of the national share of added value in exports included economies rich in natural resources (i.a., Saudi Arabia, Russia, South American countries), with a large internal market and involved in technologically advanced manufacturing processes/knowledge intensive services (including USA, Japan, Great Britain). A group with the lowest share of the domestic value added in exports (not exceeding 50%) contained small and open European economies of Luxembourg and Malta.

A Study on Time-series Clustering Analysis based on Dynamic Time Warping

A Study on Time-series Clustering Analysis based on Dynamic Time Warping

MetaboClust: Using interactive time-series cluster analysis to relate metabolomic data with perturbed pathways.

MotivationModern analytical techniques such as LC-MS, GC-MS and NMR are increasingly being used to study the underlying dynamics of biological systems by tracking changes in metabolite levels over time. Such techniques are capable of providing information on large numbers of metabolites simultaneously, a feature that is exploited in non-targeted studies. However, since the dynamics of specific metabolites are unlikely to be known a priori this presents an initial subjective challenge as to where the focus of the investigation should be. Whilst a number of feed-forward software tools are available for manipulation of metabolomic data, no tool centralizes on clustering and focus is typically directed by a workflow that is chosen in advance.ResultsWe present an interactive approach to time-course analyses and a complementary implementation in a software package, MetaboClust. This is presented through the analysis of two LC-MS time-course case studies on plants (Medicago truncatula and Alopecurus myosuroides). We demonstrate a dynamic, user-centric workflow to clustering with intrinsic visual feedback at all stages of analysis. The software is used to apply data correction, generate the time-profiles, perform exploratory statistical analysis and assign tentative metabolite identifications. Clustering is used to group metabolites in an unbiased manner, allowing pathway analysis to score metabolic pathways, based on their overlap with clusters showing interesting trends.

Time series clustering based on sparse subspace clustering algorithm and its application to daily box-office data analysis

Movie box-office research is an important work for the rapid development of the film industry, and it is also a challenging task. Our study focuses on finding the regular box-office revenue patterns. Clustering algorithm is unsupervised machine learning algorithm which classifies the data in the absence of early knowledge of the classes. Unlike static data, the time series data vary with time. The work focused on time series clustering analysis is relatively less than those focused on static data. In this paper, the sparse subspace clustering (SSC) algorithm is introduced to analyze the time series data. The SSC algorithm has a better performance both on the artificial data set and the daily box-office data than recently developed well-known clustering algorithm such as K-means and spectral clustering algorithm. On the artificial data set, SSC is more suitable for time series, whether from the angle of clustering error or visualization. On the actual data, movies are divided into five clusters by SSC algorithm, and each cluster represents a distinct type of distribution pattern. And these patterns can be used in movie recommendation, film evaluation and can guide theater exhibitors and distributors. In addition, this is the first time to apply SSC to deal with time series clustering problem and get a pleasant effect.

EXPLORING SIMILARITIES AND VARIATIONS OF HUMAN MOBILITY PATTERNS IN THE CITY OF LONDON

Abstract. The availability of new spatial data represents an unprecedented opportunity to better understand and plan cities. In particular, extensive data sets of human mobility data supply new information that can empower urbanism research to unveil how people use and visit urban places over time, overcoming traditional limitations related to the lack of large, detailed data sets. In this work, we explore patterns of similarities and spatial differences in human mobility flows in London, analysing their temporal variations in relation to the liveliness measured in a number of places. Using data sourced from the Oyster smart card and Twitter, we perform a time-series cluster analysis, exploring the similarity of temporal trends amongst places assigned to each cluster. Results suggest that differences in patterns appear to be related to the central and peripheral location of places, which present two or more temporal trends over the week. The type of transport network connecting the places (Tube, Railways, etc.) also appears to be a factor in determining significant differences. This work contributes to current urbanism research investigating the daily rhythms in cities. It also explores how to use mobility data to classify places according to their temporal features, with the aim of enhancing conventional analysis tools and integrating them with new quantitative information and methods.

Arctic amplification: evidence from a cluster analysis of temperature time series for eight latitude bands

The warming trend in the Arctic is almost twice as large as the global average in recent decades. This is known as Arctic amplification. In this paper, we perform a cluster analysis of temperature time series for eight latitude bands. Our empirical findings confirm the Arctic amplification and further shed light on this phenomenon. In particular, our investigation allows us to go beyond the simple descriptive analysis of the data. The adopted distance measures the differences between the data generating processes behind the series. Differences in the dynamic structures of the considered series are then taken into consideration allowing for a more comprehensive understanding of the phenomenon.

Time and Space Analysis of House Price in Mainland China in the Last 10 Years

In this paper, time series analysis, geographic information, cluster analysis, causality test and other techniques and means are used to analyze the housing prices of mainland China in the past 10 years. Kmeans clusters after comparison of house prices between provinces and neighboring provinces, provinces of the same category are continuously distributed on the map; the highest price area and the lowest price area on the time series are also continuously distributed on the map; the time series-based causality test found that the growth rate of house prices in six provinces was affected by the surrounding provinces, and the growth rate of house prices in one province affected the growth rate of house prices in neighboring provinces.

Measuring the Interest and the Attraction for the Heritage Over Time Using Social Big Data: The Case of Florence

In this work we will propose an innovative approach finalized to the measurement of the interest and attraction of relevant cultural touristic topics and sites using social big data. In this sense, these data can be used to forecast the touristic flows and also to construct indicators useful to the tourism management and the logistics of the heritage sites. Furthermore it is possible to measure the impact of the social events on the touristic flows by considering the interest and the attraction on them. Aim of this work is to construct innovative temporal indicators, using time series cluster analysis, which can be useful to the tourism management and in particular to develop measures for the sustainable tourism. There is a relevant emergence on the valorization and on the protection of the cultural heritage, very relevant for an higher satisfaction of life, but also on the diffusion of practices of sustainable tourism. So in one side the tourism need to be considered as an important element for the local development, on the other side there are problems related to the impact of the tourism on heritage sites. The analysis is based on the time series clustering of social media data which are useful on the identification of the size of the interest shown on the topic or site. In this specific context the interest and the attraction on the culture on the heritage sites is a relevant driver and predictor of the touristic flows and need to be analyzed also quantitatively by developing adequate indicators and methodologies. The results are applied to Florence as an international heritage city, but also on their culture.

Segmentation of Sharia Rural Banks based on Growth of the Business Performance

The Otoritas Jasa Keuangan (OJK) is an institution that is responsible to supervise bank operational in Indonesia, including those which are BPRS’s. As a part of their tasks, they do a monitoring procedure to recognize the performance progress of BPRS. A time series analytics could help in studying time-to-time change of certain performance indicators. In term of the performance indicators, not all BPRS has a good growth since they were established. Indeed many of them are growing positively, but some of them may have up-and-down pattern, even tend to be negatively diminishing. Recognizing the time series pattern of the performance indicators of BPRS is useful for OJK to identify working strategy. This recent paper focuses on segmentation process to identify groups of BPRS based on their growth pattern in multivariate time series cluster analysis cases. The result is there are 3 groups of 156 rural banks, 128 banks in the first group, 18 banks in the second, and 10 banks in the third. The general patterns of each group follow: cluster 1 is a group of banks whith positive growth in most of the variable; cluster 2 is a group that the banks that belong to this cluster have negative growth along the time, especially in the last two years; whereas Cluster 3 consist of rural banks with negative growth at the beginning of the observation period and then got rebound at the rest of the time.

Data Mining Model for Quality Control of Primary Aluminum Production Process

Received: 29 October 2012 Abstract Accepted: 28 November 2012 Traditional statistical process control approaches are less effective in dealing with multivariate and autocorrelated processes. With the continual increase in process complexity, this inefficiency is becoming more apparent. A special type of multivariate and autocorrelated process is a process occurring within a heterogeneous production environment (a variety of types of machines, pots, etc. used for the same task). This makes the quality control of such processes more difficult. The approach presented in the paper utilizes time series fitting, cluster analysis and association mining in relation to a single data mining model for the analysis of complex multivariate autocorrelated processes. The aim is to divide the production cells (machines, pots, etc.) into groups exhibiting similar behaviors. This can then be used for more effective quality control of the entire process and afterwards to analyze the reasons for this behavior. This paper includes someof the results obtained from applying the model to an actual multivariate high autocorrelated process, the production of primary aluminum using the Hall-Heroult electrolysis process. The Hall-Heroult electrolysis process is a continual process that is ongoing in several pots simultaneously. The average plant operates 300 pots. Therefore, the quality control of such a complex process faces many issues concerning monitoring and problem diagnosis. The paper describes a method for dividing the pots into control groups exhibiting similar behaviors, which can then be used in the planning phase of the quality control analysis and to make improvements within these groups and thereby within the whole process.

Monetary Integration in Africa: Is There a Business Cycles Synchronisation Between and Within Regional Economic Communities?

This paper focuses on business cycle synchronisation between Regional Economic Communities in Africa during the period 1980Q1-2016Q4. We consider a time series clustering analysis to evaluate whether the Regional Economic Communities (RECs) have similar patterns in business cycle comovements. The clustering analysis reveals five groups of countries that are relatively stable over time. Indeed, EAC is identified as a subgroup within SADC indicating that these two RECs could be merged to form a suitable monetary union in the foreseeable future. The dendogram indicates that more substantial discrepancies are observed in the clusters comparatively to the existing RECs. Finally, overlaps in membership of monetary integration may have an adverse impact on integration process in Africa. This results suggest that Africa is not ready for a common monetary union yet and it would be not surprising if the current time schedule envisaged for the launch of the single currency is postponed.

36th International Symposium on Intensive Care and Emergency Medicine : Brussels, Belgium. 15-18 March 2016.

36th International Symposium on Intensive Care and Emergency Medicine : Brussels, Belgium. 15-18 March 2016.

Online Graduate Teacher Education: Establishing an EKG for Student Success Intervention

Predicting which students enrolled in graduate online education are at-risk for failure is an arduous yet important task for teachers and administrators alike. This research reports on a statistical analysis technique using both static and dynamic variables to determine which students are at-risk and when an intervention could be most helpful during a semester. Time-series clustering analysis of online teacher education classes revealed that prediction is possible after the 10th week capturing over 78 % of at-risk students. Visual analysis of dynamic student activities shares a number of striking commonalities consistent with EKG charting. The potential exists for instructors to recognize simple graphic patterns that identify and formatively address these issues with their students. Next phases of research will apply further validation of both the models attempted and additional predictor variables.

스펙트럼분석 기반의 미기상해석모듈 평가알고리즘 제안 및 시계열 군집분석에의 응용

스펙트럼분석 기반의 미기상해석모듈 평가알고리즘 제안 및 시계열 군집분석에의 응용

Hierarchical analysis of rainfall variability across Nigeria

Rainfall in Nigeria is subjected to wide variability both in time and space. This variability has assumed a more pronounced dimension as a result of climate change. In this paper, cluster analyses were used to study rainfall variability in Nigeria. Rainfall data in 20 locations spread across the geopolitical and ecological zones of Nigeria were subjected to hierarchical cluster analysis and analysis of time series and coefficient of variation for over periods spanning 30 years. Maps of spatial variations of mean annual rainfall and mean rainfall coefficient of variation were produced using ARCGIS 10.1. Furthermore, a better understanding of temporal variation of rainfall was sought by an investigation into the time series of rainfall coefficients of variation. It was found that the southern parts of the country were given to more severe rainfall variability/fluctuations than the northern parts. The north central parts exhibited more similarity to the southern parts than the other northern locations. The relationship between average annual rainfall and the coefficient of rainfall variation was found to follow a power law with R2 value approximately 0.7. With respect to variability of annual rainfall, three zones emerged as follows: a linear relationship (R2 = 0.90) exists between coefficient of variation and average annual rainfall for the rainforest zone of the southsouth; a power law (R2 = 0.86) exists between coefficient of variation and average annual rainfall for all rainforest and derived guinea savannah zones of the southeastern and southwestern states; and a logarithmic relationship (R2 = 0.54) exists between coefficient of variation and average annual rainfall for all northern states regardless of ecological zone. Generally, in-year rainfall variability increases from the northwest to the southwest; while between-year (yearly) rainfall variability increases from the north central to the southeast. This study further confirms that rainfall variability over time follows a spatial trend within a certain arbitrary boundary.

Detection of manipulation of inter-bank overnight rate using Euclidean-based time series cluster analysis

The interbank offered rate (IBOR) is the interest rate at which banks can borrow funds from other banks in the interbank market. It is also used as the benchmark upon which rates or financial contracts for less preferred borrowers are based. In light of the recent London IBOR (LIBOR) manipulation incident, this paper seeks to address the concern that IBOR is entirely controlled by the banks. The paper focuses on the comparison between LIBOR and Singapore IBOR (SIBOR) especially with regards to the behaviour of the interest rate with time. The nature of IBORs is such that the rates submitted by the banks will naturally be similar and should not differ excessively from the market as well as the other banks. We will compare the LIBOR and SIBOR from 2005 to 2011 with respect to the one-month rates on an annual basis. The results of our study support that the SIBOR is not manipulated like LIBOR.

Groundwater levels time series sensitivity to pluviometry and air temperature: a geostatistical approach to Sfax region, Tunisia

In this paper, the pattern of groundwater level fluctuations is investigated by statistical techniques for 24 monitoring wells located in an unconfined coastal aquifer in Sfax (Tunisia) for a time period from 1997 to 2006. Firstly, a geostatistical study is performed to characterize the temporal behaviors of data sets in terms of variograms and to make predictions about the value of the groundwater level at unsampled times. Secondly, multivariate statistical methods, i.e., principal component analysis (PCA) and cluster analysis (CA) of time series of groundwater levels are used to classify groundwater hydrographs regard to identical fluctuation pattern. Three groundwater groups (A, B, and C) were identified. In group "A," water level decreases continuously throughout the study periods with rapid annual cyclic variation, whereas in group "B," the water level contains much less high-frequency variation. The wells of group "C" represents a steady and gradual increase of groundwater levels caused by the aquifer artificial recharge. Furthermore, a cross-correlation analysis is used to investigate the aquifer response to local rainfall and temperature records. The result revealed that the temperature is more affecting the variation of the groundwater level of group A wells than the rainfall. However, the second and the third groups are less affected by rainfall or temperature.

An Examination of Tourist Arrivals Dynamics Using Short-Term Time Series Data: A Space—Time Cluster Approach

The purpose of this study is to examine the development of Italian tourist areas ( circoscrizioni turistiche) through a cluster analysis of short time series. The technique is an adaptation of the functional data analysis approach developed by Abraham et al (2003), which combines spline interpolation with k-means clustering. The findings indicate the presence of two patterns (increasing and stable) averagely characterizing groups of territories. Moreover, tests of spatial contiguity suggest the presence of ‘space–time clusters’; that is, areas in the same ‘time cluster’ are also spatially contiguous. These findings appear to be more robust in particular for those series characterized by an increasing trend.

Aseismic deformation across the Hilina fault system, Hawaii, revealed by wavelet analysis of InSAR and GPS time series

The Hilina Fault System (HFS) is located on the south flank of Kilauea volcano and is thought to represent the surface expression of an unstable edifice sector that is active during seismic events such as the 1975 Kalapana earthquake. Despite its potential for hazardous landsliding and associated tsunamis, no fault activity has yet been detected by means of modern geodetic methods, since the 1975 earthquake. We present evidence from individual SAR interferograms, as well as cluster analysis and wavelet analysis of GPS and InSAR time series, which suggest an inferred differential motion at HFS. To investigate the effect of atmospheric delay on the observed differential motion, we implement a statistical approach using wavelet transforms. We jointly analyze InSAR and continuous GPS deformation data from 2003 to 2010, to estimate the likelihood that the subtle time-dependent deformation signal about the HFS scarps is not associated with the atmospheric delay. This integrated analysis reveals localized deformation components in the InSAR deformation time series that are superimposed on the coherent motion of Kilaueaʼs south flank. The statistical test suggests that at 95% confidence level, the identified differential deformation at HFS is not due to atmospheric artifacts. Since no significant shallow seismicity is observed over the study period, we suggest that this deformation occurred aseismically.

Erg and AP-1 as determinants of glucocorticoid response in acute lymphoblastic leukemia

Glucocorticoids (GCs) are among the most widely prescribed medications in clinical practice. The beneficial effects of GCs in acute lymphoblastic leukemia (ALL) are based on their ability to induce apoptosis, but the underlying transcriptional mechanisms remain poorly defined. Computational modeling has enormous potential in the understanding of biological processes such as apoptosis and the discovery of novel regulatory mechanisms. We here present an integrated analysis of gene expression kinetic profiles using microarrays from GC sensitive and resistant ALL cell lines and patients, including newly generated and previously published data sets available from the Gene Expression Omnibus. By applying time-series clustering analysis in the sensitive ALL CEM-C7-14 cells, we identified 358 differentially regulated genes that we classified into 15 kinetic profiles. We identified GC response element (GRE) sequences in 33 of the upregulated known or potential GC receptor (GR) targets. Comparative study of sensitive and resistant ALL showed distinct gene expression patterns and indicated unexpected similarities between sensitivity-restored and resistant ALL. We found that activator protein 1 (AP-1), Ets related gene (Erg) and GR pathways were differentially regulated in sensitive and resistant ALL. Erg protein levels were substantially higher in CEM-C1-15-resistant cells, c-Jun was significantly induced in sensitive cells, whereas c-Fos was expressed at low levels in both. c-Jun was recruited on the AP-1 site on the Bim promoter, whereas a transient Erg occupancy on the GR promoter was detected. Inhibition of Erg and activation of GR lead to increased apoptosis in both sensitive and resistant ALL. These novel findings significantly advance our understanding of GC sensitivity and can be used to improve therapy of leukemia.