Analyzing Granger Causality in Climate Data with Time Series Classification Methods

2017 ◽  
pp. 15-26 ◽  
Author(s):  
Christina Papagiannopoulou ◽  
Stijn Decubber ◽  
Diego G. Miralles ◽  
Matthias Demuzere ◽  
Niko E. C. Verhoest ◽  
...  
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Series Classification ◽  
Classification Methods ◽  
Climate Data

scholarly journals A non-linear Granger-causality framework to investigate climate–vegetation dynamics

2017 ◽  
Vol 10 (5) ◽  
pp. 1945-1960 ◽  
Author(s):  
Christina Papagiannopoulou ◽  
Diego G. Miralles ◽  
Stijn Decubber ◽  
Matthias Demuzere ◽  
Niko E. C. Verhoest ◽  
...  
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Vegetation Dynamics ◽  
Global Climate ◽  
Multivariate Time Series ◽  
Predictive Modelling ◽  
Experimental Results ◽  
Causality Analysis ◽  
Climate Data ◽  
Non Linear

Abstract. Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate–vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate–vegetation dynamics.

scholarly journals Uncertain Time Series Classification

2021 ◽  
Author(s):  
Michael Franklin Mbouopda
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Large Range ◽  
Time Series Classification ◽  
Classification Methods ◽  
Series Analysis ◽  
Input Time ◽  
Uncertain Time ◽  
Interpretable Classification ◽  
Input Time Series

Time series analysis has gained a lot of interest during the last decade with diverse applications in a large range of domains such as medicine, physic, and industry. The field of time series classification has been particularly active recently with the development of more and more efficient methods. However, the existing methods assume that the input time series is free of uncertainty. However, there are applications in which uncertainty is so important that it can not be neglected. This project aims to build efficient, robust, and interpretable classification methods for uncertain time series.

A DISCRIMINATIVE SHAPELETS TRANSFORMATION FOR TIME SERIES CLASSIFICATION

2014 ◽  
Vol 28 (06) ◽  
pp. 1450014 ◽  
Author(s):  
JI-DONG YUAN ◽  
ZHI-HAI WANG ◽  
MENG HAN
Keyword(s):  
Time Series ◽  
Great Influence ◽  
Classification Algorithm ◽  
Experimental Results ◽  
Time Series Classification ◽  
Classification Methods ◽  
Classification Result ◽  
Benchmark Datasets ◽  
Second Category ◽  
Pruning Technique

Time series shapelets are subsequences of time series that could be representative of a class. Shapelets-based time series classification methods can be divided into two large categories. The first category integrates shapelets selection within the process of constructing classifier; while the second category disconnects the process of finding shapelets from the classification algorithm by adopting a shapelet transformation. However, there are two important limitations of shapelet transformation. First, the number of shapelets selected for transformation has great influence on classification result, but it is difficult to decide the quantity of shapelets which yields the best data for classification. Second, similar shapelets always exist among the selected shapelets in previous algorithms. In our work, the latter problem is addressed by introducing an efficient and effective pruning technique, it filters similar shapelets and decreases the number of candidate shapelets at the same time. Then, we propose a novel shapelet coverage method to select shapelets for a given dataset. The final selected shapelets are named after Discriminative Shapelets. Our experimental results demonstrate that, on the classic benchmark datasets used for time series classification, shapelet pruning and coverage method outperforms ShapeletFilter.

scholarly journals A non-linear Granger causality framework to investigate climate–vegetation dynamics

2016 ◽  
Author(s):  
Christina Papagiannopoulou ◽  
Diego G. Miralles ◽  
Niko E. C. Verhoest ◽  
Wouter A. Dorigo ◽  
Willem Waegeman
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Vegetation Dynamics ◽  
Global Climate ◽  
Multivariate Time Series ◽  
Predictive Modelling ◽  
Experimental Results ◽  
Causality Analysis ◽  
Climate Data ◽  
Non Linear

Abstract. Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These take the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to unravel the influence of climate on vegetation dynamics. However, as advocated in this article, existing statistical methods are often too simplistic to represent complex climate–vegetation relationships due to the assumption of linearity of these relationships. Therefore, as an extension of linear Granger causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods and predictive modelling by means of random forests. Experimental results on global data sets indicate that with this framework it is possible to detect non-linear patterns that are much less visible with traditional Granger causality methods. In addition, we also discuss extensive experimental results that highlight the importance of considering the non-linear aspect of climate–vegetation dynamics.

A Frequent Pattern Based Time Series Classification Framework

2010 ◽  
Vol 32 (2) ◽  
pp. 261-266
Author(s):  
Li Wan ◽  
Jian-xin Liao ◽  
Xiao-min Zhu ◽  
Ping Ni
Keyword(s):  
Time Series ◽  
Frequent Pattern ◽  
Time Series Classification ◽  
Classification Framework
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