scholarly journals De-Trending Time Series Data for Variability Surveys

2008 ◽  
Vol 4 (S253) ◽  
pp. 370-373
Author(s):  
Dae-Won Kim ◽  
Pavlos Protopapas ◽  
Rahul Dave
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Pearson Correlation ◽  
Random Noise ◽  
Light Curves ◽  
Series Data ◽  
Linear Regression Method ◽  
Wide Field ◽  
Noise Sources

AbstractWe present an algorithm for the removal of trends in time series data. The trends could be caused by various systematic and random noise sources such as cloud passages, change of airmass or CCD noise. In order to determine the trends, we select template stars based on a hierarchical clustering algorithm. The hierarchy tree is constructed using the similarity matrix of light curves of stars whose elements are the Pearson correlation values. A new bottom-up merging algorithm is developed to extract clusters of template stars that are highly correlated among themselves, and may thus be used to identify the trends. We then use the multiple linear regression method to de-trend all individual light curves based on these determined trends. Experimental results with simulated light curves which contain artificial trends and events are presented. We also applied our algorithm to TAOS (Taiwan-American Occultation Survey) wide field data observed with a 0.5m f/1.9 telescope equipped with 2k by 2k CCD. With our approach, we successfully removed trends and increased signal to noise in TAOS light curves.

Bayesian Biclustering by dynamics: A clustering algorithm for SAGD time series data

2019 ◽  
Vol 133 ◽  
pp. 104304 ◽  
Author(s):  
Helen Pinto ◽  
Ian Gates ◽  
Xin Wang
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Series Data

Clustering Methodology for Time Series Mining

2009 ◽  
Vol 40 (1) ◽  
pp. 81-86
Author(s):  
Pēteris Grabusts ◽  
Arkady Borisov
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Similarity Measures ◽  
Longest Common Subsequence ◽  
Series Data ◽  
Time Series Clustering ◽  
Series Analysis ◽  
Time Series Mining

Clustering Methodology for Time Series MiningA time series is a sequence of real data, representing the measurements of a real variable at time intervals. Time series analysis is a sufficiently well-known task; however, in recent years research has been carried out with the purpose to try to use clustering for the intentions of time series analysis. The main motivation for representing a time series in the form of clusters is to better represent the main characteristics of the data. The central goal of the present research paper was to investigate clustering methodology for time series data mining, to explore the facilities of time series similarity measures and to use them in the analysis of time series clustering results. More complicated similarity measures include Longest Common Subsequence method (LCSS). In this paper, two tasks have been completed. The first task was to define time series similarity measures. It has been established that LCSS method gives better results in the detection of time series similarity than the Euclidean distance. The second task was to explore the facilities of the classical k-means clustering algorithm in time series clustering. As a result of the experiment a conclusion has been drawn that the results of time series clustering with the help of k-means algorithm correspond to the results obtained with LCSS method, thus the clustering results of the specific time series are adequate.

scholarly journals HUBUNGAN TINGKAT PENDAPATAN DENGAN TINGKAT KONSUMSI MASYARAKAT DI KECAMATAN BONTONOMPO KABUPATEN GOWA

2013 ◽  
Vol 9 (1) ◽  
Author(s):  
Sanusi Am Sanusi Am ◽  
Ansar Ansar
Keyword(s):  
Time Series ◽  
Significant Relationship ◽  
Time Series Data ◽  
Pearson Correlation ◽  
Income Level ◽  
Analytical Tool ◽  
Central Bureau ◽  
Series Data ◽  
Public Consumption ◽  
The Relationship

The purpose of this study is to explain the relationship between the level of income with the level of public consumption in District Bontonompo Gowa District. This research uses time series data obtained from Central Bureau of Statistics (BPS). The analytical tool used is Pearson correlation formula with the help of SPSS For Windows Release 16. The results concluded that the income level has a significant relationship to the level of public income in District Bontonompo Gowa Regency. It is expected that the Gowa Regency government can pursue programs that can encourage the creation of more and more diverse employment so that the communities of each bias can earn a decent income and meet their consumption needs

scholarly journals A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

2021 ◽  
Vol 7 ◽  
pp. e534
Author(s):  
Kristoko Dwi Hartomo ◽  
Yessica Nataliani
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Forecasting ◽  
Series Data ◽  
Improvement Rate ◽  
New Model ◽  
Average Improvement ◽  
Proposed Model

This paper aims to propose a new model for time series forecasting that combines forecasting with clustering algorithm. It introduces a new scheme to improve the forecasting results by grouping the time series data using k-means clustering algorithm. It utilizes the clustering result to get the forecasting data. There are usually some user-defined parameters affecting the forecasting results, therefore, a learning-based procedure is proposed to estimate the parameters that will be used for forecasting. This parameter value is computed in the algorithm simultaneously. The result of the experiment compared to other forecasting algorithms demonstrates good results for the proposed model. It has the smallest mean squared error of 13,007.91 and the average improvement rate of 19.83%.

scholarly journals Hybrid Models for Adaptive Allocation of Electricity for Households

2019 ◽  
Vol 9 (2S) ◽  
pp. 369-376
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Active Power ◽  
Sensor Data ◽  
Series Data ◽  
Adaptive Allocation ◽  
Forecasting Methods ◽  
Better Than

In this paper, we analyze, model, predict and cluster Global Active Power, i.e., a time series data obtained at one minute intervals from electricity sensors of a household. We analyze changes in seasonality and trends to model the data. We then compare various forecasting methods such as SARIMA and LSTM to forecast sensor data for the household and combine them to achieve a hybrid model that captures nonlinear variations better than either SARIMA or LSTM used in isolation. Finally, we cluster slices of time series data effectively using a novel clustering algorithm that is a combination of density-based and centroid-based approaches, to discover relevant subtle clusters from sensor data. Our experiments have yielded meaningful insights from the data at both a micro, day-to-day granularity, as well as a macro, weekly to monthly granularity.

scholarly journals Characterizing Sparse Asteroid Light Curves with Gaussian Processes

2021 ◽  
Vol 163 (1) ◽  
pp. 29
Author(s):  
Christina Willecke Lindberg ◽  
Daniela Huppenkothen ◽  
R. Lynne Jones ◽  
Bryce T. Bolin ◽  
Mario Jurić ◽  
...  
Keyword(s):  
Gaussian Processes ◽  
Time Series Data ◽  
Rotation Period ◽  
Light Curves ◽  
Series Data ◽  
Model Parameters ◽  
Wide Field ◽  
Pulse Profile ◽  
Rotation Periods ◽  
Period Detection

Abstract In the era of wide-field surveys like the Zwicky Transient Facility and the Rubin Observatory’s Legacy Survey of Space and Time, sparse photometric measurements constitute an increasing percentage of asteroid observations, particularly for asteroids newly discovered in these large surveys. Follow-up observations to supplement these sparse data may be prohibitively expensive in many cases, so to overcome these sampling limitations, we introduce a flexible model based on Gaussian processes to enable Bayesian parameter inference of asteroid time-series data. This model is designed to be flexible and extensible, and can model multiple asteroid properties such as the rotation period, light-curve amplitude, changing pulse profile, and magnitude changes due to the phase-angle evolution at the same time. Here, we focus on the inference of rotation periods. Based on both simulated light curves and real observations from the Zwicky Transient Facility, we show that the new model reliably infers rotational periods from sparsely sampled light curves and generally provides well-constrained posterior probability densities for the model parameters. We propose this framework as an intermediate method between fast but very limited-period detection algorithms and much more comprehensive but computationally expensive shape-modeling based on ray-tracing codes.

scholarly journals A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Saeed Aghabozorgi ◽  
Teh Ying Wah ◽  
Tutut Herawan ◽  
Hamid A. Jalab ◽  
Mohammad Amin Shaygan ◽  
...  
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Clustering Algorithms ◽  
Medical Science ◽  
Low Complexity ◽  
Series Data ◽  
Hybrid Approaches ◽  
Static Data ◽  
Important Solution

Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using thek-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets.

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