A Clustering Algorithm for Time Series Data

2006 ◽  
Author(s):  
Jian Yin ◽  
Duanning Zhou ◽  
Qiong-qiong Xie
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Series Data

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 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 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.

scholarly journals The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 69
Author(s):  
Guoliang Feng ◽  
Wei Lu ◽  
Jianhua Yang
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Cognitive Map ◽  
Fuzzy Cognitive Maps ◽  
Least Square ◽  
Fuzzy Cognitive Map ◽  
Statistical Characteristics ◽  
Series Data ◽  
The Impact

A novel design method for time series modeling and prediction with fuzzy cognitive maps (FCM) is proposed in this paper. The developed model exploits the least square method to learn the weight matrix of FCM derived from the given historical data of time series. A fuzzy c-means clustering algorithm is used to construct the concepts of the FCM. Compared with the traditional FCM, the least square fuzzy cognitive map (LSFCM) is a direct solution procedure without iterative calculations. LSFCM model is a straightforward, robust and rapid learning method, owing to its reliable and efficient. In addition, the structure of the LSFCM can be further optimized with refinements the position of the concepts for the higher prediction precision, in which the evolutionary optimization algorithm is used to find the optimal concepts. Withal, we discussed in detail the number of concepts and the parameters of activation function on the impact of FCM models. The publicly available time series data sets with different statistical characteristics coming from different areas are applied to evaluate the proposed modeling approach. The obtained results clearly show the effectiveness of the approach.

scholarly journals Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

2019 ◽  
Vol 11 (3) ◽  
pp. 793 ◽  
Author(s):  
Rashad Aliyev ◽  
Sara Salehi ◽  
Rafig Aliyev
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Demand Forecasting ◽  
Forecast Accuracy ◽  
Fuzzy Rule ◽  
Time Series Model ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Percentage Error

Receiving appropriate forecast accuracy is important in many countries’ economic activities, and developing effective and precise time series model is critical issue in tourism demand forecasting. In this paper, fuzzy rule-based system model for hotel occupancy forecasting is developed by analyzing 40 months’ time series data and applying fuzzy c-means clustering algorithm. Based on the values of root mean square error and mean absolute percentage error which are metrics for measuring forecast accuracy, it is defined that the model with 7 clusters and 4 inputs is the optimal forecasting model for hotel occupancy.

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.

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