scholarly journals Brief Communication: Breeding vectors in the phase space reconstructed from time series data

2016 ◽  
Vol 23 (3) ◽  
pp. 137-141 ◽  
Author(s):  
Erin Lynch ◽  
Daniel Kaufman ◽  
A. Surjalal Sharma ◽  
Eugenia Kalnay ◽  
Kayo Ide
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Evolution Equations ◽  
Nearest Neighbor ◽  
Time Series Data ◽  
Series Data ◽  
Nonlinear Instability ◽  
Bred Vectors ◽  
Dynamical Properties ◽  
A New Technique

Abstract. Bred vectors characterize the nonlinear instability of dynamical systems and so far have been computed only for systems with known evolution equations. In this article, bred vectors are computed from a single time series data using time-delay embedding, with a new technique, nearest-neighbor breeding. Since the dynamical properties of the standard and nearest-neighbor breeding are shown to be similar, this provides a new and novel way to model and predict sudden transitions in systems represented by time series data alone.

scholarly journals Brief Communication: Breeding vectors in the phase space reconstructed from time series data

2015 ◽  
Vol 2 (4) ◽  
pp. 1301-1315
Author(s):  
E. Lynch ◽  
D. Kaufman ◽  
A. S. Sharma ◽  
E. Kalnay ◽  
K. Ide
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Evolution Equations ◽  
Nearest Neighbor ◽  
Time Series Data ◽  
Series Data ◽  
Nonlinear Instability ◽  
Bred Vectors ◽  
Dynamical Properties ◽  
A New Technique

Abstract. Bred vectors characterize the nonlinear instability of dynamical systems and so far have been computed only for systems with known evolution equations. In this article, bred vectors are computed from a single time series data using time-delay embedding, with a new technique, nearest-neighbor breeding. Since the dynamical properties of the standard and nearest-neighbor breeding are shown to be similar, this provides a new and novel way to model and predict sudden transitions in systems represented by time series data alone.

Study of Forecasting on Time Series Data by Nearest Neighbor Method.

2001 ◽  
Vol 27 (2) ◽  
pp. 272-274
Author(s):  
Yoshitomo Hanakuma ◽  
Junzou Yamamoto
Keyword(s):  
Time Series ◽  
Nearest Neighbor ◽  
Time Series Data ◽  
Series Data

Chaotic Identification of Hourly and Daily Water Level Time Series Data in Different Areas of Elevation at Pahang River

2021 ◽  
Vol 12 (3) ◽  
pp. 2427-2435
Author(s):  
Adib Mashuri Et.al
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Water Level ◽  
Time Scale ◽  
Time Series Data ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Level Data ◽  
Water Level Data ◽  
Daily Time

This study focused on chaotic analysis of water level data in different elevations located in the highland and lowland areas. This research was conducted considering the uncertain water level caused by the river flow from highland to lowland areas. The analysis was conducted using the data collected from the four area stations along Pahang River on different time scales which were hourly and daily time series data. The resulted findings were relevant to be used by the local authorities in water resource management in these areas. Two methods were used for the analysis process which included Cao method and phase space plot. Both methods are based on phase space reconstruction that is referring to reconstruction of one dimensional data (water level data) to d-dimensional phase space in order to determine the dynamics of the system. The combination of parameters  and d is required in phase space reconstruction. Results showed that (i) the combination of phase space reconstruction’s parameters gave a higher value of parameters by using hourly time scale compared to daily time scale for different elevation; (ii) different elevation gave impact on the values of phase space reconstructions’ parameters; (iii) chaotic dynamics existed using Cao method and phase space plot for different elevation and time scale. Hence, water level data with different time scale from different elevation in Pahang River can be used in the development of prediction model based on chaos approach.

Phase space reconstruction using input-output time series data

1999 ◽  
Vol 60 (4) ◽  
pp. 4008-4013 ◽  
Author(s):  
David M. Walker ◽  
Nicholas B. Tufillaro
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Time Series Data ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Input Output ◽  
Output Time

scholarly journals Synchronized Time Profile Similarity in Applications to Nearest Neighbor Classification

2020 ◽  
Author(s):  
Qimin Liu
Keyword(s):  
Time Series ◽  
Dynamic Time Warping ◽  
Nearest Neighbor ◽  
Time Series Data ◽  
Time Profile ◽  
Series Data ◽  
Time Warping ◽  
Nearest Neighbor Classification ◽  
Dynamic Time ◽  
Neighbor Classification

One of the existing approaches to time series classification exploits the time profiles using the original data with synchronization instead of model-implied data. Synchronization aligns inter-individual data from different time points to account for potential phase offsets and nonstationarity in the data. Such synchronization has been applied in psychology: For example, coordinated motion between two individuals exchanging information was used as a predictor and outcome of psychological processes. Synchronization also affords better classification outcomes, as discussed in the data mining community, through aligning the data to reveal the maximally shared profile underlying two compared data sequences. For inter-individual comparison of univariate time series data, existing similarity indices include Euclidean distances and squared correlations. For synchronization, we introduce dynamic time warping and window-crossed lagging. The current study compares the Euclidean distance and the squared correlation before and after synchronization using window-crossed lagging and dynamic time warping in applications to one-nearest-neighbor classification tasks. Discussion, limitations, and future directions are provided.

Recognition of workers’ actions from time-series signal images using deep convolutional neural network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Omobolanle Ruth Ogunseiju ◽  
Johnson Olayiwola ◽  
Abiola Abosede Akanmu ◽  
Chukwuma Nnaji
Keyword(s):  
Time Series ◽  
Action Recognition ◽  
Nearest Neighbor ◽  
Time Series Data ◽  
Computational Cost ◽  
Construction Workers ◽  
Series Data ◽  
K Nearest Neighbor ◽  
Deep Convolutional Neural Networks ◽  
Content Type

PurposeConstruction action recognition is essential to efficiently manage productivity, health and safety risks. These can be achieved by tracking and monitoring construction work. This study aims to examine the performance of a variant of deep convolutional neural networks (CNNs) for recognizing actions of construction workers from images of signals of time-series data.Design/methodology/approachThis paper adopts Inception v1 to classify actions involved in carpentry and painting activities from images of motion data. Augmented time-series data from wearable sensors attached to worker's lower arms are converted to signal images to train an Inception v1 network. Performance of Inception v1 is compared with the highest performing supervised learning classifier, k-nearest neighbor (KNN).FindingsResults show that the performance of Inception v1 network improved when trained with signal images of the augmented data but at a high computational cost. Inception v1 network and KNN achieved an accuracy of 95.2% and 99.8%, respectively when trained with 50-fold augmented carpentry dataset. The accuracy of Inception v1 and KNN with 10-fold painting augmented dataset is 95.3% and 97.1%, respectively.Research limitations/implicationsOnly acceleration data of the lower arm of the two trades were used for action recognition. Each signal image comprises 20 datasets.Originality/valueLittle has been reported on recognizing construction workers' actions from signal images. This study adds value to the existing literature, in particular by providing insights into the extent to which a deep CNN can classify subtasks from patterns in signal images compared to a traditional best performing shallow network.

scholarly journals Hydrologic Variations and Stochastic Modeling of Runoff in Zoige Wetland in the Eastern Tibetan Plateau

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Guanghua Qin ◽  
Hongxia Li ◽  
Zejiang Zhou ◽  
Kechao Song ◽  
Li Zhang
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Nearest Neighbor ◽  
Time Series Data ◽  
Water Source ◽  
Annual Runoff ◽  
Series Data ◽  
Eastern Tibetan Plateau ◽  
Double Peaks ◽  
Zoige Wetland

Hydrological time series data (1988–2008) of the Hei River, the main water source to Zoige wetland in the Eastern Tibetan Plateau, were investigated. Results showed that the runoff distribution of Hei River varies with the relative change in amplitude (Cm=15.9) and the absolute change in amplitude (ΔQ=37.1 m3/s) during the year. There was a significant decreasing trend since 1988 with annual runoff of 20.0 m3/s (1988–1994), 19.0 m3/s (1995–2000), and 15.2 m3/s (2001–2008). There were double peaks in runoff during the water year: the highest peak in the period of 1988–2000 occurred in July while in the period of 2001–2008 it occurred in October. Shifting peak flow means less water quantity in wetland during growing season. Nearest neighbor bootstrapping regressive method was used to predict daily runoff of the Hei River. Model results show that it was fitted with 94.23% ofR2for daily time series, which can provide a basis for the development and utilization of regional water resources.

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