scholarly journals Data Fusion of Multivariate Time Series Based on Local Maximum Weighted Coefficient

2020 ◽  
Author(s):  
Chen Diao
Keyword(s):  
Time Series ◽  
Data Fusion ◽  
Multivariate Time Series ◽  
Local Maximum ◽  
Original System ◽  
Experimental Results ◽  
Motion Information ◽  
Lorenz Model ◽  
Ecg Signals ◽  
Fusion Data

In this paper, a novel fusion data algorithm is proposed via local Maximum weighted coefficient algorithm. For effectively fusing multivariate time series and reserving the motion information of original system, the weighted coefficients are rationally estimated the fusion state. Experimental results show that the algorithm can obtain desirable results on Lorenz model and multi-lead ECG signals.

scholarly journals Data Fusion of Multivariate Time Series: Application to Noisy 12-Lead ECG Signals

2018 ◽  
Vol 9 (1) ◽  
pp. 105 ◽  
Author(s):  
Chen Diao ◽  
Bin Wang ◽  
Ning Cai
Keyword(s):  
Data Fusion ◽  
Linear Prediction ◽  
Fuzzy Inference ◽  
Multivariate Time Series ◽  
Quality Characteristics ◽  
Prediction Algorithm ◽  
Ecg Signals ◽  
Fusion Data ◽  
Ecg Signal Processing ◽  
Inference Systems

Twelve-lead Electrocardiograph (ECG) signals fusion is crucial for further ECG signal processing. In this paper, based on the idea of the local weighted linear prediction algorithm, a novel fusion data algorithm is proposed, which was applied in data fusion of the 12-lead ECG signals. In order to analyze the signal quality comprehensively, the quality characteristics should be adequately retained in the final fused result. In our algorithm, the values for the weighted coefficient of state points were closely related to the final fused result. Thus, two fuzzy inference systems were designed to calculate the weighted coefficients. For the sake of assessing the performance of our method, synthetic ECG signals and realistic ECG signals were applied in the experiments. Experimental results indicate that our method can fuse the 12-lead ECG signals effectively with inherit the quality characteristics of original ECG signals inherited properly.

scholarly journals Two Birds with One Stone: Series Saliency for Accurate and Interpretable Multivariate Time Series Forecasting

2021 ◽  
Author(s):  
Qingyi Pan ◽  
Wenbo Hu ◽  
Ning Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Data Augmentation ◽  
Multivariate Time Series ◽  
Time Series Forecasting ◽  
Experimental Results ◽  
Learning Methods ◽  
Forecasting Accuracy ◽  
Sliding Windows ◽  
Scheme Of Series

It is important yet challenging to perform accurate and interpretable time series forecasting. Though deep learning methods can boost forecasting accuracy, they often sacrifice interpretability. In this paper, we present a new scheme of series saliency to boost both accuracy and interpretability. By extracting series images from sliding windows of the time series, we design series saliency as a mixup strategy with a learnable mask between the series images and their perturbed versions. Series saliency is model agnostic and performs as an adaptive data augmentation method for training deep models. Moreover, by slightly changing the objective, we optimize series saliency to find a mask for interpretable forecasting in both feature and time dimensions. Experimental results on several real datasets demonstrate that series saliency is effective to produce accurate time-series forecasting results as well as generate temporal interpretations.

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 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.

Time-varying copula models for financial time series

2016 ◽  
Vol 48 (A) ◽  
pp. 159-180
Author(s):  
Rüdiger Kiesel ◽  
Magda Mroz ◽  
Ulrich Stadtmüller
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Multivariate Time Series ◽  
Local Maximum ◽  
Time Varying ◽  
Monte Carlo Simulation Study ◽  
Copula Models ◽  
Copula Theory ◽  
Statistical Homogeneity ◽  
Financial Time

AbstractWe perform an analysis of the potential time inhomogeneity in the dependence between multiple financial time series. To this end, we use the framework of copula theory and tackle the question of whether dependencies in such a case can be assumed constant throughout time or rather have to be modeled in a time-inhomogeneous way. We focus on parametric copula models and suitable inference techniques in the context of a special copula-based multivariate time series model. A recent result due to Chan et al. (2009) is used to derive the joint limiting distribution of local maximum-likelihood estimators on overlapping samples. By restricting the overlap to be fixed, we establish the limiting law of the maximum of the estimator series. Based on the limiting distributions, we develop statistical homogeneity tests, and investigate their local power properties. A Monte Carlo simulation study demonstrates that bootstrapped variance estimates are needed in finite samples. Empirical analyses on real-world financial data finally confirm that time-varying parameters are an exception rather than the rule.

scholarly journals F4: An All-Purpose Tool for Multivariate Time Series Classification

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3051
Author(s):  
Ángel López-Oriona ◽  
José A. Vilar
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Discrete Wavelet ◽  
Time Series Classification ◽  
East Anglia ◽  
Ecg Signals ◽  
Novel Approach ◽  
Cross Spectral Density ◽  
The University ◽  
Electrocardiogram Ecg

We propose Fast Forest of Flexible Features (F4), a novel approach for classifying multivariate time series, which is aimed to discriminate between underlying generating processes. This goal has barely been addressed in the literature. F4 consists of two steps. First, a set of features based on the quantile cross-spectral density and the maximum overlap discrete wavelet transform are extracted from each series. Second, a random forest is fed with the extracted features. An extensive simulation study shows that F4 outperforms some powerful classifiers in a wide variety of situations, including stationary and nonstationary series. The proposed method is also capable of successfully discriminating between electrocardiogram (ECG) signals of healthy subjects and those with myocardial infarction condition. Additionally, despite lacking shape-based information, F4 attains state-of-the-art results in some datasets of the University of East Anglia (UEA) multivariate time series classification archive.

Sign in / Sign up

Export Citation Format

Share Document