scholarly journals Right-Censored Time Series Modeling by Modified Semi-Parametric A-Spline Estimator

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1586
Author(s):  
Dursun Aydın ◽  
Syed Ejaz Ahmed ◽  
Ersin Yılmaz
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Synthetic Data ◽  
Semiparametric Regression ◽  
Real Data ◽  
Series Data ◽  
Monte Carlo Simulation Study ◽  
Kaplan Meier ◽  
Spline Fitting ◽  
Censored Observations

This paper focuses on the adaptive spline (A-spline) fitting of the semiparametric regression model to time series data with right-censored observations. Typically, there are two main problems that need to be solved in such a case: dealing with censored data and obtaining a proper A-spline estimator for the components of the semiparametric model. The first problem is traditionally solved by the synthetic data approach based on the Kaplan–Meier estimator. In practice, although the synthetic data technique is one of the most widely used solutions for right-censored observations, the transformed data’s structure is distorted, especially for heavily censored datasets, due to the nature of the approach. In this paper, we introduced a modified semiparametric estimator based on the A-spline approach to overcome data irregularity with minimum information loss and to resolve the second problem described above. In addition, the semiparametric B-spline estimator was used as a benchmark method to gauge the success of the A-spline estimator. To this end, a detailed Monte Carlo simulation study and a real data sample were carried out to evaluate the performance of the proposed estimator and to make a practical comparison.

scholarly journals How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 95 ◽  
Author(s):  
Johannes Stübinger ◽  
Katharina Adler
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Structural Breaks ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Real Data ◽  
Linear Mapping ◽  
Series Data ◽  
Lag Effects ◽  
Silver Metal

This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences—structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature.

scholarly journals MITRE: predicting host status from microbiota time-series data

2018 ◽  
Author(s):  
Elijah Bogart ◽  
Richard Creswell ◽  
Georg K. Gerber
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Data ◽  
Synthetic Data ◽  
Black Box ◽  
Series Data ◽  
Learning Approaches ◽  
Rule Engine ◽  
Microbiome Composition ◽  
Host Status

AbstractLongitudinal studies are crucial for discovering casual relationships between the microbiome and human disease. We present Microbiome Interpretable Temporal Rule Engine (MITRE), the first machine learning method specifically designed for predicting host status from microbiome time-series data. Our method maintains interpretability by learning predictive rules over automatically inferred time-periods and phylogenetically related microbes. We validate MITRE’s performance on semi-synthetic data, and five real datasets measuring microbiome composition over time in infant and adult cohorts. Our results demonstrate that MITRE performs on par or outperforms “black box” machine learning approaches, providing a powerful new tool enabling discovery of biologically interpretable relationships between microbiome and human host.

scholarly journals Time series forecasting of styrene price using a hybrid ARIMA and neural network model

2019 ◽  
Vol 10 (3) ◽  
pp. 915
Author(s):  
Ali Ebrahimi Ghahnavieh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Real Data ◽  
Time Series Forecasting ◽  
Series Data ◽  
Data Sets ◽  
Combined Model ◽  
Future Behavior ◽  
Hybrid Forecasting ◽  
Ann Models

Every player in the market has a greater need to know about the smallest change in the market. Therefore, the ability to see what is ahead is a valuable advantage. The purpose of this research is to make an attempt to understand the behavioral patterns and try to find a new hybrid forecasting approach based on ARIMA-ANN for estimating styrene price. The time series analysis and forecasting is an essential tool which could be widely useful for finding the significant characteristics for making future decisions. In this study ARIMA, ANN and Hybrid ARIMA-ANN models were applied to evaluate the previous behavior of a time series data, in order to make interpretations about its future behavior for styrene price. Experimental results with real data sets show that the combined model can be most suitable to improve forecasting accurateness rather than traditional time series forecasting methodologies. As a subset of the literature, the small number of studies have been done to realize the new forecasting methods for forecasting styrene price.

scholarly journals TimeTrial: An Interactive Application for Optimizing the Design and Analysis of Transcriptomic Time-Series Data in Circadian Biology Research

2020 ◽  
Vol 35 (5) ◽  
pp. 439-451 ◽  
Author(s):  
Elan Ness-Cohn ◽  
Marta Iwanaszko ◽  
William L. Kath ◽  
Ravi Allada ◽  
Rosemary Braun
Keyword(s):  
Time Series ◽  
Experimental Design ◽  
Time Series Data ◽  
Synthetic Data ◽  
Optimal Choice ◽  
Series Data ◽  
Detection Accuracy ◽  
Periodic Patterns ◽  
Detection Algorithms ◽  
The Impact

The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues, coordinating physiological processes. The effect of this rhythm on health has generated increasing interest in discovering genes under circadian control by searching for periodic patterns in transcriptomic time-series experiments. While algorithms for detecting cycling transcripts have advanced, there remains little guidance quantifying the effect of experimental design and analysis choices on cycling detection accuracy. We present TimeTrial, a user-friendly benchmarking framework using both real and synthetic data to investigate cycle detection algorithms’ performance and improve circadian experimental design. Results show that the optimal choice of analysis method depends on the sampling scheme, noise level, and shape of the waveform of interest and provides guidance on the impact of sampling frequency and duration on cycling detection accuracy. The TimeTrial software is freely available for download and may also be accessed through a web interface. By supplying a tool to vary and optimize experimental design considerations, TimeTrial will enhance circadian transcriptomics studies.

Random order autoregressive time series model with structural break

2020 ◽  
Vol 15 (3) ◽  
pp. 225-237
Author(s):  
Saurabh Kumar ◽  
Jitendra Kumar ◽  
Vikas Kumar Sharma ◽  
Varun Agiwal
Keyword(s):  
Time Series ◽  
Structural Breaks ◽  
Time Series Data ◽  
Structural Break ◽  
Random Order ◽  
Real Data ◽  
Series Data ◽  
Model Parameters ◽  
Multiple Time ◽  
Multiple Time Points

This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model parameters are discussed in both classical and Bayesian frameworks. Since the joint posterior of the parameters is not analytically tractable, we employ a Markov Chain Monte Carlo method, Gibbs sampling to simulate posterior sample. To verify the order change, a hypotheses test is constructed using posterior probability and compared with that of without breaks. The methodologies proposed here are illustrated by means of simulation study and a real data analysis.

scholarly journals Pattern discovery in time series using autoencoder in comparison to nonlearning approaches

2021 ◽  
pp. 1-20
Author(s):  
Fabian Kai-Dietrich Noering ◽  
Yannik Schroeder ◽  
Konstantin Jonas ◽  
Frank Klawonn
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Pattern Discovery ◽  
Computing Time ◽  
Real Life ◽  
Synthetic Data ◽  
Series Data ◽  
Data Sets ◽  
Research Fields ◽  
The Matrix

In technical systems the analysis of similar situations is a promising technique to gain information about the system’s state, its health or wearing. Very often, situations cannot be defined but need to be discovered as recurrent patterns within time series data of the system under consideration. This paper addresses the assessment of different approaches to discover frequent variable-length patterns in time series. Because of the success of artificial neural networks (NN) in various research fields, a special issue of this work is the applicability of NNs to the problem of pattern discovery in time series. Therefore we applied and adapted a Convolutional Autoencoder and compared it to classical nonlearning approaches based on Dynamic Time Warping, based on time series discretization as well as based on the Matrix Profile. These nonlearning approaches have also been adapted, to fulfill our requirements like the discovery of potentially time scaled patterns from noisy time series. We showed the performance (quality, computing time, effort of parametrization) of those approaches in an extensive test with synthetic data sets. Additionally the transferability to other data sets is tested by using real life vehicle data. We demonstrated the ability of Convolutional Autoencoders to discover patterns in an unsupervised way. Furthermore the tests showed, that the Autoencoder is able to discover patterns with a similar quality like classical nonlearning approaches.

scholarly journals Time Series Clustering Based on the K-Means Algorithm

2020 ◽  
Vol 1 (3) ◽  
pp. 1-7
Author(s):  
Oleg Kobylin ◽  
Vyacheslav Lyashenko
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Time Series Data ◽  
Real Data ◽  
Series Data ◽  
High Similarity ◽  
Time Series Clustering ◽  
Data Presentation

Time series is one of the forms of data presentation that is used in many studies. It is convenient, easy and informative. Clustering is one of the tasks of data processing. Thus, the most relevant currently are methods for clustering time series. Clustering time series data aims to create clusters with high similarity within a cluster and low similarity between clusters. This work is devoted to clustering time series. Various methods of time series clustering are considered. Examples are given for real data.

Based on ETEO Pattern Abnormal Event Detection in Wireless Sensor Networks

2014 ◽  
Vol 926-930 ◽  
pp. 1886-1889
Author(s):  
Bo Tian ◽  
Dian Hong Wang ◽  
Fen Xiong Chen ◽  
Zheng Pu Zhang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Time Series Data ◽  
Feature Space ◽  
Real Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Edge Operator ◽  
Abnormal Events

This paper presents a new algorithm for the detection of abnormal events in Wireless Sensor Networks (WSN). Abnormal events are sets of data points that correspond to interesting patterns in the underlying phenomenon that the network monitors. This algorithm is inspired from time-series data mining techniques and transforms a stream of sensor readings into an Extension Temporal Edge Operator (ETEO) of time series pattern representation, and then extracts the three eigenvalue of each sub-pattern, that is, patterns length, patterns slope and patterns mean to map time series to feature space, and finally uses local outlier factor to detect abnormal pattern in this feature space. Experiments on synthetic and real data show that the definition of pattern outlier is reasonable and this algorithm is efficient to detect outliers in WSN.

Deconvolution of seismograms by the autoregressive method

Geophysics ◽  
1983 ◽  
Vol 48 (2) ◽  
pp. 229-233 ◽  
Author(s):  
G. Jayachandran Nair
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Time Series Data ◽  
Single Channel ◽  
Synthetic Data ◽  
Ar Model ◽  
Underground Explosion ◽  
Series Data ◽  
Inverse Filtering ◽  
Seismic Wavelet

The purpose of deconvolution in seismology is to estimate the basic seismic wavelet and the transfer function of the transmission medium. In reflection seismology, this transfer function refers to the reflectivity function, while in seismograms of earthquakes and explosions it represents the combined effects of the source crust and the receiver crust responses along with the attenuation function. Some of the techniques used for deconvolution of discrete time series data are Wiener inverse filtering (Robinson and Treitel, 1967), homomorphic deconvolution (Ulrych, 1971), and Kalman filtering (Crump, 1974). In the present paper, a method of deconvolution of single‐channel seismic data based on an autoregressive (AR) model of the time series is discussed. With it one can estimate the primary pulse and the deconvolution function simultaneously in an objective manner. Examples are provided to substantiate the applicability of the method using synthetic data simulating single and multiple explosions. The method is also applied to actual data for a presumed underground explosion from Eastern Kazakh.

scholarly journals Detecting and Quantifying Natural Selection at Two Linked Loci from Time Series Data of Allele Frequencies with Forward-in-Time Simulations

Genetics ◽  
2020 ◽  
Vol 216 (2) ◽  
pp. 521-541
Author(s):  
Zhangyi He ◽  
Xiaoyang Dai ◽  
Mark Beaumont ◽  
Feng Yu
Keyword(s):  
Time Series ◽  
Natural Selection ◽  
Time Series Data ◽  
Genetic Recombination ◽  
Real Data ◽  
Series Data ◽  
Accurate Estimation ◽  
Inference Procedure ◽  
Posterior Probability Distribution ◽  
Recent Action

Recent advances in DNA sequencing techniques have made it possible to monitor genomes in great detail over time. This improvement provides an opportunity for us to study natural selection based on time serial samples of genomes while accounting for genetic recombination effect and local linkage information. Such time series genomic data allow for more accurate estimation of population genetic parameters and hypothesis testing on the recent action of natural selection. In this work, we develop a novel Bayesian statistical framework for inferring natural selection at a pair of linked loci by capitalising on the temporal aspect of DNA data with the additional flexibility of modeling the sampled chromosomes that contain unknown alleles. Our approach is built on a hidden Markov model where the underlying process is a two-locus Wright-Fisher diffusion with selection, which enables us to explicitly model genetic recombination and local linkage. The posterior probability distribution for selection coefficients is computed by applying the particle marginal Metropolis-Hastings algorithm, which allows us to efficiently calculate the likelihood. We evaluate the performance of our Bayesian inference procedure through extensive simulations, showing that our approach can deliver accurate estimates of selection coefficients, and the addition of genetic recombination and local linkage brings about significant improvement in the inference of natural selection. We also illustrate the utility of our method on real data with an application to ancient DNA data associated with white spotting patterns in horses.

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