scholarly journals Detection of Local Mixing in Time-Series Data Using Permutation Entropy

2021 ◽  
Author(s):  
Elizabeth Bradley ◽  
Michael Neuder ◽  
Joshua Garland ◽  
James White ◽  
Edward Dlugokencky
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Ice Core ◽  
Series Data ◽  
Permutation Entropy ◽  
Information Theoretic ◽  
Mauna Loa ◽  
Model Free ◽  
Multiple Copies ◽  
Chemistry Experiment

<p>  While it is tempting in experimental practice to seek as high a  data rate as possible, oversampling can become an issue if one takes measurements too densely.  These effects can take many  forms, some of which are easy to detect: e.g., when the data sequence contains multiple copies of the same measured value.  In other situations, as when there is mixing—in the measurement apparatus and/or the system itself—oversampling effects can be harder to detect.  We propose a novel, model-free technique to detect local mixing in time series using an information-theoretic technique called permutation entropy.  By varying the temporal resolution of the calculation and analyzing the patterns in the results, we can determine whether the data are mixed locally, and on what scale.  This can be used by practitioners to choose appropriate lower bounds on scales at which to measure or report data.  After validating this technique on several synthetic examples, we demonstrate its effectiveness on data from a chemistry experiment, methane records from Mauna Loa, and an Antarctic ice core.</p>

scholarly journals An Information Theoretic Approach for Creating 3D Spatial Images from 4D Time Series Data

2017 ◽  
Vol 23 (S1) ◽  
pp. 100-101
Author(s):  
Willy Wriggers ◽  
Julio Kovacs ◽  
Federica Castellani ◽  
P. Thomas Vernier ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Information Theoretic Approach

A Novel Approach to Time Series Forecasting Using Model-Free Adaptive Control Framework

2020 ◽  
Author(s):  
Meenakshi Narayan ◽  
Ann Majewicz Fey
Keyword(s):  
Time Series ◽  
Adaptive Control ◽  
Real Time ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Force Sensor ◽  
Sensor Data ◽  
Series Data ◽  
Model Free ◽  
Control Framework

Abstract Sensor data predictions could significantly improve the accuracy and effectiveness of modern control systems; however, existing machine learning and advanced statistical techniques to forecast time series data require significant computational resources which is not ideal for real-time applications. In this paper, we propose a novel forecasting technique called Compact Form Dynamic Linearization Model-Free Prediction (CFDL-MFP) which is derived from the existing model-free adaptive control framework. This approach enables near real-time forecasts of seconds-worth of time-series data due to its basis as an optimal control problem. The performance of the CFDL-MFP algorithm was evaluated using four real datasets including: force sensor readings from surgical needle, ECG measurements for heart rate, and atmospheric temperature and Nile water level recordings. On average, the forecast accuracy of CFDL-MFP was 28% better than the benchmark Autoregressive Integrated Moving Average (ARIMA) algorithm. The maximum computation time of CFDL-MFP was 49.1ms which was 170 times faster than ARIMA. Forecasts were best for deterministic data patterns, such as the ECG data, with a minimum average root mean squared error of (0.2±0.2).

scholarly journals An Approach for Reconstruction of Realistic Economic Data Based on Frequency Characteristics between IMFs

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Hao Du ◽  
Hao Gong ◽  
Suyue Han ◽  
Peng Zheng ◽  
Bin Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Low Frequency ◽  
Test Method ◽  
Series Data ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Economic Data ◽  
Index Method

Reconstruction of realistic economic data often causes social economists to analyze the underlying driving factors in time-series data or to study volatility. The intrinsic complexity of time-series data interests and attracts social economists. This paper proposes the bilateral permutation entropy (BPE) index method to solve the problem based on partly ensemble empirical mode decomposition (PEEMD), which was proposed as a novel data analysis method for nonlinear and nonstationary time series compared with the T-test method. First, PEEMD is extended to the case of gold price analysis in this paper for decomposition into several independent intrinsic mode functions (IMFs), from high to low frequency. Second, IMFs comprise three parts, including a high-frequency part, low-frequency part, and the whole trend based on a fine-to-coarse reconstruction by the BPE index method and the T-test method. Then, this paper conducts a correlation analysis on the basis of the reconstructed data and the related affected macroeconomic factors, including global gold production, world crude oil prices, and world inflation. Finally, the BPE index method is evidently a vitally significant technique for time-series data analysis in terms of reconstructed IMFs to obtain realistic data.

scholarly journals Inferring causality in biological oscillators

2021 ◽  
Author(s):  
Jonathan Tyler ◽  
Daniel Forger ◽  
JaeKyoung Kim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Supplementary Information ◽  
Series Data ◽  
Biological Study ◽  
Oscillatory Systems ◽  
Model Based ◽  
Model Free ◽  
Oscillatory Networks ◽  
Inference Methods

Abstract Motivation Fundamental to biological study is identifying regulatory interactions. The recent surge in time-series data collection in biology provides a unique opportunity to infer regulations computationally. However, when components oscillate, model-free inference methods, while easily implemented, struggle to distinguish periodic synchrony and causality. Alternatively, model-based methods test the reproducibility of time series given a specific model but require inefficient simulations and have limited applicability. Results We develop an inference method based on a general model of molecular, neuronal, and ecological oscillatory systems that merges the advantages of both model-based and model-free methods, namely accuracy, broad applicability, and usability. Our method successfully infers the positive and negative regulations within various oscillatory networks, e.g., the repressilator and a network of cofactors at the pS2 promoter, outperforming popular inference methods. Availability We provide a computational package, ION (Inferring Oscillatory Networks), that users can easily apply to noisy, oscillatory time series to uncover the mechanisms by which diverse systems generate oscillations. Accompanying MATLAB code under a BSD-style license and examples are available at ttps://github.com/Mathbiomed/ION. Additionally, the code is available under a CC-BY 4.0 License at https://doi.org/10.6084/m9.figshare.16431408.v1. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Interpretable Models for Understanding Immersive Simulations

2020 ◽  
Author(s):  
Nicholas Hoernle ◽  
Kobi Gal ◽  
Barbara Grosz ◽  
Leilah Lyons ◽  
Ada Ren ◽  
...  
Keyword(s):  
Time Series ◽  
Bayesian Approach ◽  
Time Series Data ◽  
Statistical Information ◽  
Information Criteria ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Information Theoretic ◽  
Interpretable Models ◽  
Fully Bayesian

This paper describes methods for comparative evaluation of the interpretability of models of high dimensional time series data inferred by unsupervised machine learning algorithms. The time series data used in this investigation were logs from an immersive simulation like those commonly used in education and healthcare training. The structures learnt by the models provide representations of participants' activities in the simulation which are intended to be meaningful to people's interpretation. To choose the model that induces the best representation, we designed two interpretability tests, each of which evaluates the extent to which a model’s output aligns with people’s expectations or intuitions of what has occurred in the simulation. We compared the performance of the models on these interpretability tests to their performance on statistical information criteria. We show that the models that optimize interpretability quality differ from those that optimize (statistical) information theoretic criteria. Furthermore, we found that a model using a fully Bayesian approach performed well on both the statistical and human-interpretability measures. The Bayesian approach is a good candidate for fully automated model selection, i.e., when direct empirical investigations of interpretability are costly or infeasible.

scholarly journals Detection of local mixing in time-series data using permutation entropy

2021 ◽  
Vol 103 (2) ◽  
Author(s):  
Michael Neuder ◽  
Elizabeth Bradley ◽  
Edward Dlugokencky ◽  
James W. C. White ◽  
Joshua Garland
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Permutation Entropy
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