Random order autoregressive time series model with structural break

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.

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

Algorithms ◽

10.3390/a13040095 ◽

2020 ◽

Vol 13 (4) ◽

pp. 95 ◽

Cited By ~ 1

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.

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Inferring Population Genetics Parameters of Evolving Viruses Using Time-series Data

10.1101/437483 ◽

2018 ◽

Author(s):

Tal Zinger ◽

Pleuni S. Pennings ◽

Adi Stern

Keyword(s):

Time Series ◽

Population Size ◽

Deep Sequencing ◽

Time Series Data ◽

Simulated Data ◽

Ease Of Use ◽

Series Data ◽

Multiple Time ◽

Sequencing Data ◽

Multiple Time Points

1AbstractWith the advent of deep sequencing techniques, it is now possible to track the evolution of viruses with ever-increasing detail. Here we present FITS (Flexible Inference from Time-Series) – a computational framework that allows inference of either the fitness of a mutation, the mutation rate or the population size from genomic time-series sequencing data. FITS was designed first and foremost for analysis of either short-term Evolve & Resequence (E&R) experiments, or for rapidly recombining populations of viruses. We thoroughly explore the performance of FITS on noisy simulated data, and highlight its ability to infer meaningful information even in those circumstances. In particular FITS is able to categorize a mutation as Advantageous, Neutral or Deleterious. We next apply FITS to empirical data from an E&R experiment on poliovirus where parameters were determined experimentally and demonstrate extremely high accuracy in inference. We highlight the ease of use of FITS for step-wise or iterative inference of mutation rates, population size, and fitness values for each mutation sequenced, when deep sequencing data is available at multiple time-points.AvailabilityFITS is written in C++ and is available both with a highly user friendly graphical user interface but also as a command line program that allows parallel high throughput analyses. Source code, binaries (Windows and Mac) and complementary scripts, are available from GitHub at https://github.com/SternLabTAU/[email protected]

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Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽

10.3390/ai2010005 ◽

2021 ◽

Vol 2 (1) ◽

pp. 48-70

Author(s):

Wei Ming Tan ◽

T. Hui Teo

Keyword(s):

Neural Network ◽

Time Series ◽

Time Series Data ◽

Remaining Useful Life ◽

Sensor Data ◽

Series Data ◽

Multiple Time ◽

Data Set ◽

Form Complex ◽

Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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Testing Structural Break in the Relationship Between Exchange Rate and Macroeconomic Variables

Vision The Journal of Business Perspective ◽

10.1177/0972262919850933 ◽

2019 ◽

Vol 23 (4) ◽

pp. 442-453 ◽

Cited By ~ 1

Author(s):

Saidia Jeelani ◽

Joity Tomar ◽

Tapas Das ◽

Seshanwita Das

Keyword(s):

Exchange Rate ◽

Structural Breaks ◽

Time Series Data ◽

Structural Break ◽

Series Data ◽

Ripple Effect ◽

Macroeconomic Factors ◽

Parameter Instability ◽

Macroeconomic Variables ◽

The Relationship

The article aims to study the relationship between those macroeconomic factors that the affect (INR/USD) exchange rate (ER). Time series data of 40 years on ER, GDP, inflation, interest rate (IR), FDI, money supply, trade balance (TB) and terms of trade (ToT) have been collected from the RBI website. The considered model has suggested that only inflation, TB and ToT have influenced the ER significantly during the study period. Other macroeconomic variables such as GDP, FDI and IR have not significantly influenced the ER during the study period. The model is robust and does not suffer from residual heteroscedasticity, autocorrelation and non-normality. Sometimes the relationship between ER and macroeconomic variables gets affected by major economic events. For example, the Southeast Asian crisis caused by currency depreciation in 1997 and sub-prime loan crisis of 2008 severely strained the national economies. Any global economic turmoil will affect different economic variables through ripple effect and this, in turn, will affect the ER of different economies differently. The article has also diagnosed whether there is any structural break or not in the model by applying Chow’s Breakpoint Test and have obtained multiple breaks between 2003 and 2009. The existence of structural breaks during 2003–2009 is explained by the fact that volume of crude oil imported by India is high and oil price rise led to a deficit in the TB alarmingly, which caused a structural break or parameter instability.

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A framework to analyze multiple time series data: A case study with Streptomyces coelicolor

Journal of Industrial Microbiology & Biotechnology ◽

10.1007/s10295-005-0034-7 ◽

2005 ◽

Vol 33 (2) ◽

pp. 159-172 ◽

Cited By ~ 24

Author(s):

Sarika Mehra ◽

Wei Lian ◽

Karthik P. Jayapal ◽

Salim P. Charaniya ◽

David H. Sherman ◽

...

Keyword(s):

Time Series ◽

Time Series Data ◽

Streptomyces Coelicolor ◽

Series Data ◽

Multiple Time ◽

Multiple Time Series

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Cosinor Analysis for Temperature Time Series Data of Long Duration

Biological Research For Nursing ◽

10.1177/1099800407303509 ◽

2007 ◽

Vol 9 (1) ◽

pp. 30-41 ◽

Cited By ~ 7

Author(s):

Nikhil S. Padhye ◽

Sandra K. Hanneman

Keyword(s):

Time Series ◽

Time Series Data ◽

Model Fit ◽

Series Data ◽

Model Parameters ◽

Cosinor Analysis ◽

Long Time ◽

Improved Model ◽

Data Length ◽

Cosinor Models

The application of cosinor models to long time series requires special attention. With increasing length of the time series, the presence of noise and drifts in rhythm parameters from cycle to cycle lead to rapid deterioration of cosinor models. The sensitivity of amplitude and model-fit to the data length is demonstrated for body temperature data from ambulatory menstrual cycling and menopausal women and from ambulatory male swine. It follows that amplitude comparisons between studies cannot be made independent of consideration of the data length. Cosinor analysis may be carried out on serial-sections of the series for improved model-fit and for tracking changes in rhythm parameters. Noise and drift reduction can also be achieved by folding the series onto a single cycle, which leads to substantial gains in the model-fit but lowers the amplitude. Central values of model parameters are negligibly changed by consideration of the autoregressive nature of residuals.

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