scholarly journals Comparison of time series clustering methods and application to power consumption pattern clustering

2020 ◽  
Vol 27 (6) ◽  
pp. 589-602
Author(s):  
Jaehwi Kim ◽  
Jaehee Kim
Keyword(s):  
Time Series ◽  
Power Consumption ◽  
Consumption Pattern ◽  
Clustering Methods ◽  
Time Series Clustering ◽  
Pattern Clustering

scholarly journals Revisiting Climate Region Definitions via Clustering

2009 ◽  
Vol 22 (7) ◽  
pp. 1787-1800 ◽  
Author(s):  
Robert Lund ◽  
Bo Li
Keyword(s):  
Time Series ◽  
Clustering Methods ◽  
Distance Metric ◽  
Climatic Zones ◽  
Time Series Clustering ◽  
Climate Region ◽  
Covariate Effects ◽  
Weather Stations ◽  
Climatic Time Series ◽  
Natural Way

Abstract This paper introduces a new distance metric that enables the clustering of general climatic time series. Clustering methods have been frequently used to partition a domain of interest into distinct climatic zones. However, previous techniques have neglected the time series (autocorrelation) component and have also handled seasonal features in a suboptimal way. The distance proposed here incorporates the seasonal mean and autocorrelation structures of the series in a natural way; moreover, trends and covariate effects can be considered. As an important by-product, the methods can be used to statistically assess whether two stations can serve as reference stations for one another. The methods are illustrated by partitioning 292 weather stations within the state of Colorado into six different zones.

scholarly journals Comparison Study of Time Series Clustering Methods

2009 ◽  
Vol 22 (6) ◽  
pp. 1203-1214 ◽  
Author(s):  
Han-Woom Hong ◽  
Min-Jeong Park ◽  
Sin-Sup Cho
Keyword(s):  
Time Series ◽  
Comparison Study ◽  
Clustering Methods ◽  
Time Series Clustering

scholarly journals Similarity Preserving Representation Learning for Time Series Clustering

2019 ◽  
Author(s):  
Qi Lei ◽  
Jinfeng Yi ◽  
Roman Vaculin ◽  
Lingfei Wu ◽  
Inderjit S. Dhillon
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Clustering Algorithms ◽  
Representation Learning ◽  
Feature Representation ◽  
Series Data ◽  
Similarity Matrix ◽  
Clustering Methods ◽  
Time Series Clustering ◽  
Partially Observed

A considerable amount of clustering algorithms take instance-feature matrices as their inputs. As such, they cannot directly analyze time series data due to its temporal nature, usually unequal lengths, and complex properties. This is a great pity since many of these algorithms are effective, robust, efficient, and easy to use. In this paper, we bridge this gap by proposing an efficient representation learning framework that is able to convert a set of time series with various lengths to an instance-feature matrix. In particular, we guarantee that the pairwise similarities between time series are well preserved after the transformation , thus the learned feature representation is particularly suitable for the time series clustering task. Given a set of $n$ time series, we first construct an $n\times n$ partially-observed similarity matrix by randomly sampling $\mathcal{O}(n \log n)$ pairs of time series and computing their pairwise similarities. We then propose an efficient algorithm that solves a non-convex and NP-hard problem to learn new features based on the partially-observed similarity matrix. By conducting extensive empirical studies, we demonstrate that the proposed framework is much more effective, efficient, and flexible compared to other state-of-the-art clustering methods.

scholarly journals Time Series Clustering of Electricity Demand for Industrial Areas on Smart Grid

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2377
Author(s):  
Heung-gu Son ◽  
Yunsun Kim ◽  
Sahm Kim
Keyword(s):  
Time Series ◽  
Smart Grid ◽  
Moving Average ◽  
Demand Forecasting ◽  
Prediction Method ◽  
Electricity Demand ◽  
Percentage Error ◽  
Autoregressive Moving Average ◽  
Clustering Methods ◽  
Time Series Clustering

This study forecasts electricity demand in a smart grid environment. We present a prediction method that uses a combination of forecasting values based on time-series clustering. The clustering of normalized periodogram-based distances and autocorrelation-based distances are proposed as the time-series clustering methods. Trigonometrical transformation, Box–Cox transformation, autoregressive moving average (ARMA) errors, trend and seasonal components (TBATS), double seasonal Holt–Winters (DSHW), fractional autoregressive integrated moving average (FARIMA), ARIMA with regression (Reg-ARIMA), and neural network nonlinear autoregressive (NN-AR) are used for demand forecasting based on clustering. The results show that the time-series clustering method performs better than the method using the total amount of electricity demand in terms of the mean absolute percentage error (MAPE).

scholarly journals Examining of the countries in terms of water resources along with the effect of climate change by time series clustering

2021 ◽  
Author(s):  
özlem akay
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Water Resources ◽  
Water Cycle ◽  
Clustering Methods ◽  
Time Series Clustering ◽  
Risk Levels ◽  
Partitioning Around Medoids ◽  
Living Things ◽  
Life Itself

Abstract Today, the problem of climate change is addressed in many ways. The most important effect of climate change is the disruption of the water cycle. Therefore, the location and timing of the water resources in the world are changing. Water is an indispensable resource that connects all living things together and directly affects their lives. Water is not only a biological requirement for man, but also for economic, social, and cultural life itself. However, this resource, which is of vital importance, exists unfortunately in a limited amount on earth. This study aims to identify similar countries in terms of water resources by taking into account the changes in precipitation amounts together with climate change. For this purpose, a time series clustering analysis was conducted to precipitation amounts of 21 countries between 2009-2017 with the Hierarchical clustering and Partitioning Around Medoids (PAM) clustering methods. Countries are divided into two clusters and they were evaluated according to their water risk levels and populations. It should be noted that countries with low precipitation, high risk of water, and a high population should take urgent measures for climate change.

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