Journal articles: 'Time-series analysis Inference'

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Wiggins, C. H., and I. Nemenman. "Process pathway inference via time series analysis." Experimental Mechanics 43, no. 3 (2003): 361–70. http://dx.doi.org/10.1007/bf02410536.

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Western, Bruce, and Meredith Kleykamp. "A Bayesian Change Point Model for Historical Time Series Analysis." Political Analysis 12, no. 4 (2004): 354–74. http://dx.doi.org/10.1093/pan/mph023.

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Political relationships often vary over time, but standard models ignore temporal variation in regression relationships. We describe a Bayesian model that treats the change point in a time series as a parameter to be estimated. In this model, inference for the regression coefficients reflects prior uncertainty about the location of the change point. Inferences about regression coefficients, unconditional on the change-point location, can be obtained by simulation methods. The model is illustrated in an analysis of real wage growth in 18 OECD countries from 1965–1992.

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Gluhovsky, Alexander, and Ernest Agee. "On the Analysis of Atmospheric and Climatic Time Series." Journal of Applied Meteorology and Climatology 46, no. 7 (2007): 1125–29. http://dx.doi.org/10.1175/jam2512.1.

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Abstract Linear parametric models are commonly assumed and used for unknown data-generating mechanisms. This study demonstrates the value of inferring statistics of meteorological and climatological time series by using a computer-intensive subsampling method that allows one to avoid time series analysis anchored in parametric models with imposed perceived physical assumptions. A first-order autoregressive model, typically adopted as the default model for correlated time series in climate studies, has been selected and altered with a nonlinear component to provide insight into possible errors in estimation due to nonlinearities in the real data-generating mechanism. The nonlinearity undetected by basic diagnostic procedures is shown to invalidate statistical inference based on the linear model, whereas the inference derived through subsampling remains valid. It is argued that subsampling and other resampling methods are preferable in complex dependent-data situations that are typical for atmospheric and climatic series when the real data-generating mechanism is unknown.

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Boretti, Alberto. "Analysis of Segmented Sea level Time Series." Applied Sciences 10, no. 2 (2020): 625. http://dx.doi.org/10.3390/app10020625.

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Records of measurements of sea levels from tide gauges are often “segmented”, i.e., obtained by composing segments originating from the same or different instruments, in the same or different locations, or suffering from other biases that prevent the coupling. A technique is proposed, based on data mining, the application of break-point alignment techniques, and similarity with other segmented and non-segmented records for the same water basin, to quality flag the segmented records. This prevents the inference of incorrect trends for the rate of rise and the acceleration of the sea levels for these segmented records. The technique is applied to the four long-term trend tide gauges of the Indian Ocean, Aden, Karachi, Mumbai, and Fremantle, with three of them segmented.

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Kim, Seong-Eun, Michael K. Behr, Demba Ba, and Emery N. Brown. "State-space multitaper time-frequency analysis." Proceedings of the National Academy of Sciences 115, no. 1 (2017): E5—E14. http://dx.doi.org/10.1073/pnas.1702877115.

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Time series are an important data class that includes recordings ranging from radio emissions, seismic activity, global positioning data, and stock prices to EEG measurements, vital signs, and voice recordings. Rapid growth in sensor and recording technologies is increasing the production of time series data and the importance of rapid, accurate analyses. Time series data are commonly analyzed using time-varying spectral methods to characterize their nonstationary and often oscillatory structure. Current methods provide local estimates of data features. However, they do not offer a statistical inference framework that applies to the entire time series. The important advances that we report are state-space multitaper (SS-MT) methods, which provide a statistical inference framework for time-varying spectral analysis of nonstationary time series. We model nonstationary time series as a sequence of second-order stationary Gaussian processes defined on nonoverlapping intervals. We use a frequency-domain random-walk model to relate the spectral representations of the Gaussian processes across intervals. The SS-MT algorithm efficiently computes spectral updates using parallel 1D complex Kalman filters. An expectation–maximization algorithm computes static and dynamic model parameter estimates. We test the framework in time-varying spectral analyses of simulated time series and EEG recordings from patients receiving general anesthesia. Relative to standard multitaper (MT), SS-MT gave enhanced spectral resolution and noise reduction (>10 dB) and allowed statistical comparisons of spectral properties among arbitrary time series segments. SS-MT also extracts time-domain estimates of signal components. The SS-MT paradigm is a broadly applicable, empirical Bayes’ framework for statistical inference that can help ensure accurate, reproducible findings from nonstationary time series analyses.

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Shellman, Stephen M. "Time Series Intervals and Statistical Inference: The Effects of Temporal Aggregation on Event Data Analysis." Political Analysis 12, no. 1 (2004): 97–104. http://dx.doi.org/10.1093/pan/mpg017.

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While many areas of research in political science draw inferences from temporally aggregated data, rarely have researchers explored how temporal aggregation biases parameter estimates. With some notable exceptions (Freeman 1989, Political Analysis 1:61–98; Alt et al. 2001, Political Analysis 9:21–44; Thomas 2002, “Event Data Analysis and Threats from Temporal Aggregation”) political science studies largely ignore how temporal aggregation affects our inferences. This article expands upon others' work on this issue by assessing the effect of temporal aggregation decisions on vector autoregressive (VAR) parameter estimates, significance levels, Granger causality tests, and impulse response functions. While the study is relevant to all fields in political science, the results directly apply to event data studies of conflict and cooperation. The findings imply that political scientists should be wary of the impact that temporal aggregation has on statistical inference.

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Kořenek, Jakub, and Jaroslav Hlinka. "Causality in Reversed Time Series: Reversed or Conserved?" Entropy 23, no. 8 (2021): 1067. http://dx.doi.org/10.3390/e23081067.

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The inference of causal relations between observable phenomena is paramount across scientific disciplines; however, the means for such enterprise without experimental manipulation are limited. A commonly applied principle is that of the cause preceding and predicting the effect, taking into account other circumstances. Intuitively, when the temporal order of events is reverted, one would expect the cause and effect to apparently switch roles. This was previously demonstrated in bivariate linear systems and used in design of improved causal inference scores, while such behaviour in linear systems has been put in contrast with nonlinear chaotic systems where the inferred causal direction appears unchanged under time reversal. The presented work explores the conditions under which the causal reversal happens—either perfectly, approximately, or not at all—using theoretical analysis, low-dimensional examples, and network simulations, focusing on the simplified yet illustrative linear vector autoregressive process of order one. We start with a theoretical analysis that demonstrates that a perfect coupling reversal under time reversal occurs only under very specific conditions, followed up by constructing low-dimensional examples where indeed the dominant causal direction is even conserved rather than reversed. Finally, simulations of random as well as realistically motivated network coupling patterns from brain and climate show that level of coupling reversal and conservation can be well predicted by asymmetry and anormality indices introduced based on the theoretical analysis of the problem. The consequences for causal inference are discussed.

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Azumah, Karim, Ananda Omutokoh Kube, and Bashiru Imoro Ibn Saeed. "Functional Time Series Analysis of Land Surface Temperature." International Journal of Statistics and Probability 9, no. 5 (2020): 61. http://dx.doi.org/10.5539/ijsp.v9n5p61.

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Parametric modeling imposes rigid assumption on abstraction of physical characteristics of a phenomenon, which in case of model misspecification could give erroneous results. To address the drawbacks, efforts have been channeled on semi-parametric and nonparametric modeling and inference. This study focuses on constructing an estimator and consequently modeling a meteorological temperature time series first by constructing a penalized spline estimator based on cubic splines. The penalized spline estimator proposed, which are known to impose very minimal restrictions on estimation process, provides good fits to observed data with very attractive properties namely consistent as observed in values of the Mean Squared Error from the analysis. The results of our simulations compared favorably with the empirical analysis on average monthly meteorological temperature data obtained from Climate Knowledge Portal World Bank Organization on Ghana for periods 1901-2016.

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Linden, Ariel. "A matching framework to improve causal inference in interrupted time-series analysis." Journal of Evaluation in Clinical Practice 24, no. 2 (2017): 408–15. http://dx.doi.org/10.1111/jep.12874.

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Linden, Ariel. "Using permutation tests to enhance causal inference in interrupted time series analysis." Journal of Evaluation in Clinical Practice 24, no. 3 (2018): 496–501. http://dx.doi.org/10.1111/jep.12899.

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Firat, Mahmut, Mustafa Erkan Turan, and Mehmet Ali Yurdusev. "Comparative analysis of fuzzy inference systems for water consumption time series prediction." Journal of Hydrology 374, no. 3-4 (2009): 235–41. http://dx.doi.org/10.1016/j.jhydrol.2009.06.013.

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Liang, X. San. "Normalized Multivariate Time Series Causality Analysis and Causal Graph Reconstruction." Entropy 23, no. 6 (2021): 679. http://dx.doi.org/10.3390/e23060679.

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Causality analysis is an important problem lying at the heart of science, and is of particular importance in data science and machine learning. An endeavor during the past 16 years viewing causality as a real physical notion so as to formulate it from first principles, however, seems to have gone unnoticed. This study introduces to the community this line of work, with a long-due generalization of the information flow-based bivariate time series causal inference to multivariate series, based on the recent advance in theoretical development. The resulting formula is transparent, and can be implemented as a computationally very efficient algorithm for application. It can be normalized and tested for statistical significance. Different from the previous work along this line where only information flows are estimated, here an algorithm is also implemented to quantify the influence of a unit to itself. While this forms a challenge in some causal inferences, here it comes naturally, and hence the identification of self-loops in a causal graph is fulfilled automatically as the causalities along edges are inferred. To demonstrate the power of the approach, presented here are two applications in extreme situations. The first is a network of multivariate processes buried in heavy noises (with the noise-to-signal ratio exceeding 100), and the second a network with nearly synchronized chaotic oscillators. In both graphs, confounding processes exist. While it seems to be a challenge to reconstruct from given series these causal graphs, an easy application of the algorithm immediately reveals the desideratum. Particularly, the confounding processes have been accurately differentiated. Considering the surge of interest in the community, this study is very timely.

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Graves, T., R. B. Gramacy, C. L. E. Franzke, and N. W. Watkins. "Efficient Bayesian inference for natural time series using ARFIMA processes." Nonlinear Processes in Geophysics 22, no. 6 (2015): 679–700. http://dx.doi.org/10.5194/npg-22-679-2015.

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Abstract. Many geophysical quantities, such as atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long memory (LM). LM implies that these quantities experience non-trivial temporal memory, which potentially not only enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LM. In this paper we present a modern and systematic approach to the inference of LM. We use the flexible autoregressive fractional integrated moving average (ARFIMA) model, which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LM, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g., short-memory effects) can be integrated over in order to focus on long-memory parameters and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data and the central England temperature (CET) time series, with favorable comparison to the standard estimators. For CET we also extend our method to seasonal long memory.

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Kiesel, Rüdiger, Magda Mroz, and Ulrich Stadtmüller. "Time-varying copula models for financial time series." Advances in Applied Probability 48, A (2016): 159–80. http://dx.doi.org/10.1017/apr.2016.48.

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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.

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Amornbunchornvej, Chainarong, Elena Zheleva, and Tanya Berger-Wolf. "Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (2021): 1–30. http://dx.doi.org/10.1145/3441452.

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Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. The assumption of fixed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop Variable-lag Granger causality and Variable-lag Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allow causes to influence effects with arbitrary time delays. In addition, we propose methods for inferring both Variable-lag Granger causality and Transfer Entropy relations. In our approaches, we utilize an optimal warping path of Dynamic Time Warping to infer variable-lag causal relations. We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets. Our approaches can be applied in any domain of time series analysis. The software of this work is available in the R-CRAN package: VLTimeCausality.

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Du, Sizhen, Guojie Song, Lei Han, and Haikun Hong. "Temporal Causal Inference with Time Lag." Neural Computation 30, no. 1 (2018): 271–91. http://dx.doi.org/10.1162/neco_a_01028.

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Accurate causal inference among time series helps to better understand the interactive scheme behind the temporal variables. For time series analysis, an unavoidable issue is the existence of time lag among different temporal variables. That is, past evidence would take some time to cause a future effect instead of an immediate response. To model this process, existing approaches commonly adopt a prefixed time window to define the lag. However, in many real-world applications, this parameter may vary among different time series, and it is hard to be predefined with a fixed value. In this letter, we propose to learn the causal relations as well as the lag among different time series simultaneously from data. Specifically, we develop a probabilistic decomposed slab-and-spike (DSS) model to perform the inference by applying a pair of decomposed spike-and-slab variables for the model coefficients, where the first variable is used to estimate the causal relationship and the second one captures the lag information among different temporal variables. For parameter inference, we propose an efficient expectation propagation (EP) algorithm to solve the DSS model. Experimental results conducted on both synthetic and real-world problems demonstrate the effectiveness of the proposed method. The revealed time lag can be well validated by the domain knowledge within the real-world applications.

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Commandeur, Jacques J. F., Frits D. Bijleveld, Ruth Bergel-Hayat, Constantinos Antoniou, George Yannis, and Eleonora Papadimitriou. "On statistical inference in time series analysis of the evolution of road safety." Accident Analysis & Prevention 60 (November 2013): 424–34. http://dx.doi.org/10.1016/j.aap.2012.11.006.

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Kokoszka, Piotr, Gregory Rice, and Han Lin Shang. "Inference for the autocovariance of a functional time series under conditional heteroscedasticity." Journal of Multivariate Analysis 162 (November 2017): 32–50. http://dx.doi.org/10.1016/j.jmva.2017.08.004.

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Foreman, M. G. G., J. Y. Cherniawsky, and V. A. Ballantyne. "Versatile Harmonic Tidal Analysis: Improvements and Applications." Journal of Atmospheric and Oceanic Technology 26, no. 4 (2009): 806–17. http://dx.doi.org/10.1175/2008jtecho615.1.

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Abstract New computer software that permits more versatility in the harmonic analysis of tidal time series is described and tested. Specific improvements to traditional methods include the analysis of randomly sampled and/or multiyear data; more accurate nodal correction, inference, and astronomical argument adjustments through direct incorporation in the least squares matrix; multiconstituent inferences from a single reference constituent; correlation matrices and error estimates that facilitate decisions on the selection of constituents for the analysis; and a single program that analyzes one- or two-dimensional time series. This new methodology is evaluated through comparisons with results from old techniques and then applied to two problems that could not have been accurately solved with older software. They are (i) the analysis of ocean station temperature time series spanning 25 yr, and (ii) the analysis of satellite altimetry from a ground track whose proximity to land has led to significant data dropout. This new software is free as part of the Institute of Ocean Sciences (IOS) Tidal Package and can be downloaded, along with sample input data and an explanatory readme file.

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Lu, Jonathan, Bianca Dumitrascu, Ian C. McDowell, et al. "Causal network inference from gene transcriptional time-series response to glucocorticoids." PLOS Computational Biology 17, no. 1 (2021): e1008223. http://dx.doi.org/10.1371/journal.pcbi.1008223.

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Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS.

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Veraart, Almut E. D. "Modeling, simulation and inference for multivariate time series of counts using trawl processes." Journal of Multivariate Analysis 169 (January 2019): 110–29. http://dx.doi.org/10.1016/j.jmva.2018.08.012.

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Thomakos, Dimitrios D., and Hossein Hassani. "Using singular spectrum analysis for inference on seasonal time series with seasonal unit roots." International Journal of Computational Economics and Econometrics 10, no. 2 (2020): 149. http://dx.doi.org/10.1504/ijcee.2020.10029489.

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Thomakos, Dimitrios D., and Hossein Hassani. "Using singular spectrum analysis for inference on seasonal time series with seasonal unit roots." International Journal of Computational Economics and Econometrics 10, no. 2 (2020): 149. http://dx.doi.org/10.1504/ijcee.2020.107371.

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Li, Shi, Bhramar Mukherjee, Stuart Batterman, and Malay Ghosh. "Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference." Biometrics 69, no. 4 (2013): 925–36. http://dx.doi.org/10.1111/biom.12102.

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Linden, Ariel. "Using group-based trajectory modelling to enhance causal inference in interrupted time series analysis." Journal of Evaluation in Clinical Practice 24, no. 3 (2018): 502–7. http://dx.doi.org/10.1111/jep.12934.

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Bullmore, Ed, Chris Long, John Suckling, et al. "Colored noise and computational inference in fMRI time series analysis: resampling methods in time and wavelet domains." NeuroImage 13, no. 6 (2001): 86. http://dx.doi.org/10.1016/s1053-8119(01)91429-6.

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Goswami, Bedartha. "A Brief Introduction to Nonlinear Time Series Analysis and Recurrence Plots." Vibration 2, no. 4 (2019): 332–68. http://dx.doi.org/10.3390/vibration2040021.

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Nonlinear time series analysis gained prominence from the late 1980s on, primarily because of its ability to characterize, analyze, and predict nontrivial features in data sets that stem from a wide range of fields such as finance, music, human physiology, cognitive science, astrophysics, climate, and engineering. More recently, recurrence plots, initially proposed as a visual tool for the analysis of complex systems, have proven to be a powerful framework to quantify and reveal nontrivial dynamical features in time series data. This tutorial review provides a brief introduction to the fundamentals of nonlinear time series analysis, before discussing in greater detail a few (out of the many existing) approaches of recurrence plot-based analysis of time series. In particular, it focusses on recurrence plot-based measures which characterize dynamical features such as determinism, synchronization, and regime changes. The concept of surrogate-based hypothesis testing, which is crucial to drawing any inference from data analyses, is also discussed. Finally, the presented recurrence plot approaches are applied to two climatic indices related to the equatorial and North Pacific regions, and their dynamical behavior and their interrelations are investigated.

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Xiao, Zhijie. "LIKELIHOOD-BASED INFERENCE IN TRENDING TIME SERIES WITH A ROOT NEAR UNITY." Econometric Theory 17, no. 6 (2001): 1082–112. http://dx.doi.org/10.1017/s0266466601176036.

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This paper studies likelihood-based estimation and tests for autoregressive time series models with deterministic trends and general disturbance distributions. In particular, a joint estimation of the trend coefficients and the autoregressive parameter is considered. Asymptotic analysis on the M-estimators is provided. It is shown that the limiting distributions of these estimators involve nonlinear equation systems of Brownian motions even for the simple case of least squares regression. Unit root tests based on M-estimation are also considered, and extensions of the Neyman–Pearson test are studied. The finite sample performance of these estimators and testing procedures is examined by Monte Carlo experiments.

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Kato, Risa, and Takayuki Shiohama. "Model and Variable Selection Procedures for Semiparametric Time Series Regression." Journal of Probability and Statistics 2009 (2009): 1–37. http://dx.doi.org/10.1155/2009/487194.

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Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.

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Friedrich, Marina, Eric Beutner, Hanno Reuvers, et al. "A statistical analysis of time trends in atmospheric ethane." Climatic Change 162, no. 1 (2020): 105–25. http://dx.doi.org/10.1007/s10584-020-02806-2.

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Abstract Ethane is the most abundant non-methane hydrocarbon in the Earth’s atmosphere and an important precursor of tropospheric ozone through various chemical pathways. Ethane is also an indirect greenhouse gas (global warming potential), influencing the atmospheric lifetime of methane through the consumption of the hydroxyl radical (OH). Understanding the development of trends and identifying trend reversals in atmospheric ethane is therefore crucial. Our dataset consists of four series of daily ethane columns. As with many other decadal time series, our data are characterized by autocorrelation, heteroskedasticity, and seasonal effects. Additionally, missing observations due to instrument failure or unfavorable measurement conditions are common in such series. The goal of this paper is therefore to analyze trends in atmospheric ethane with statistical tools that correctly address these data features. We present selected methods designed for the analysis of time trends and trend reversals. We consider bootstrap inference on broken linear trends and smoothly varying nonlinear trends. In particular, for the broken trend model, we propose a bootstrap method for inference on the break location and the corresponding changes in slope. For the smooth trend model, we construct simultaneous confidence bands around the nonparametrically estimated trend. Our autoregressive wild bootstrap approach, combined with a seasonal filter, is able to handle all issues mentioned above (we provide R code for all proposed methods on https://www.stephansmeekes.nl/code.).

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Zhaomin, Lv, Jiang Qingchao, and Yan Xuefeng. "Batch Process Monitoring Based on Multisubspace Multiway Principal Component Analysis and Time-Series Bayesian Inference." Industrial & Engineering Chemistry Research 53, no. 15 (2014): 6457–66. http://dx.doi.org/10.1021/ie403576c.

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Linden, Ariel. "Combining synthetic controls and interrupted time series analysis to improve causal inference in program evaluation." Journal of Evaluation in Clinical Practice 24, no. 2 (2018): 447–53. http://dx.doi.org/10.1111/jep.12882.

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Bullmore, Ed, Chris Long, John Suckling, et al. "Colored noise and computational inference in neurophysiological (fMRI) time series analysis: Resampling methods in time and wavelet domains." Human Brain Mapping 12, no. 2 (2001): 61–78. http://dx.doi.org/10.1002/1097-0193(200102)12:2<61::aid-hbm1004>3.0.co;2-w.

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Doan, T. K., J. Haslett, and A. C. Parnell. "Joint inference of misaligned irregular time series with application to Greenland ice core data." Advances in Statistical Climatology, Meteorology and Oceanography 1, no. 1 (2015): 15–27. http://dx.doi.org/10.5194/ascmo-1-15-2015.

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Abstract. Ice cores provide insight into the past climate over many millennia. Due to ice compaction, the raw data for any single core are irregular in time. Multiple cores have different irregularities; and when considered together, they are misaligned in time. After processing, such data are made available to researchers as regular time series: a data product. Typically, these cores are independently processed. This paper considers a fast Bayesian method for the joint processing of multiple irregular series. This is shown to be more efficient than the independent alternative. Furthermore, our explicit framework permits a reliable modelling of the impact of the multiple sources of uncertainty. The methodology is illustrated with the analysis of a pair of ice cores. Our data products, in the form of posterior marginals or joint distributions on an arbitrary temporal grid, are finite Gaussian mixtures. We can also produce process histories to study non-linear functionals of interest. More generally, the concept of joint analysis via hierarchical Gaussian process models can be widely extended, as the models used can be viewed within the larger context of continuous space–time processes.

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Ai, Dongmei, Xiaoxin Li, Gang Liu, Xiaoyi Liang, and Li Xia. "Constructing the Microbial Association Network from Large-Scale Time Series Data Using Granger Causality." Genes 10, no. 3 (2019): 216. http://dx.doi.org/10.3390/genes10030216.

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The increasing availability of large-scale time series data allows the inference of microbial community dynamics by association network analysis. However, correlation-based association network analyses are noninformative of causal, mediating and time-dependent relationships between microbial community functional factors. To address this insufficiency, we introduced the Granger causality model to the analysis of a recent marine microbial time series dataset. We systematically constructed a directed acyclic network, representing both internal and external causal relationships among the microbial and environmental factors. We further optimized the network by removing false causal associations using the conditional Granger causality. The final network was visualized as a Granger graph, which was analyzed to identify causal relationships driven by key functional operators in the environment, such as Gammaproteobacteria, which was Granger caused by total organic nitrogen and primary production (p < 0.05 and Q < 0.05).

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Weiß, Christian H. "Measures of Dispersion and Serial Dependence in Categorical Time Series." Econometrics 7, no. 2 (2019): 17. http://dx.doi.org/10.3390/econometrics7020017.

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The analysis and modeling of categorical time series requires quantifying the extent of dispersion and serial dependence. The dispersion of categorical data is commonly measured by Gini index or entropy, but also the recently proposed extropy measure can be used for this purpose. Regarding signed serial dependence in categorical time series, we consider three types of κ -measures. By analyzing bias properties, it is shown that always one of the κ -measures is related to one of the above-mentioned dispersion measures. For doing statistical inference based on the sample versions of these dispersion and dependence measures, knowledge on their distribution is required. Therefore, we study the asymptotic distributions and bias corrections of the considered dispersion and dependence measures, and we investigate the finite-sample performance of the resulting asymptotic approximations with simulations. The application of the measures is illustrated with real-data examples from politics, economics and biology.

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Shelatkar, Tejas, Stephen Tondale, Swaraj Yadav, and Sheetal Ahir. "Web Traffic Time Series Forecasting using ARIMA and LSTM RNN." ITM Web of Conferences 32 (2020): 03017. http://dx.doi.org/10.1051/itmconf/20203203017.

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Nowadays, web traffic forecasting is a major problem as this can cause setbacks to the workings of major websites. Time-series forecasting has been a hot topic for research. Predicting future time series values is one of the most difficult problems in the industry. The time series field encompasses many different issues, ranging from inference and analysis to forecasting and classification. Forecasting the network traffic and displaying it in a dashboard that updates in real-time would be the most efficient way to convey the information. Creating a Dashboard would help in monitoring and analyzing real-time data. In this day and age, we are too dependent on Google server but if we want to host a server for large users we could have predicted the number of users from previous years to avoid server breakdown. Time Series forecasting is crucial to multiple domains. ARIMA; LSTM RNN; web traffic; prediction;time series;

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Iwata, Kentaro, Asako Doi, and Chisato Miyakoshi. "Was school closure effective in mitigating coronavirus disease 2019 (COVID-19)? Time series analysis using Bayesian inference." International Journal of Infectious Diseases 99 (October 2020): 57–61. http://dx.doi.org/10.1016/j.ijid.2020.07.052.

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Zhu, Tingting, Hamza Javed, and David A. Clifton. "Comparison of parametric and non‐parametric Bayesian inference for fusing sensory estimates in physiological time‐series analysis." Healthcare Technology Letters 8, no. 2 (2021): 25–30. http://dx.doi.org/10.1049/htl2.12003.

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Shaharudin, S. M., N. Ahmad, and N. H. Zainuddin. "Modified singular spectrum analysis in identifying rainfall trend over peninsular Malaysia." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (2019): 283. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp283-293.

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<p>Identifying the local time scale of the torrential rainfall pattern through Singular Spectrum Analysis (SSA) is useful to separate the trend and noise components. However, SSA poses two main issues which are torrential rainfall time series data have coinciding singular values and the leading components from eigenvector obtained from the decomposing time series matrix are usually assesed by graphical inference lacking in a specific statistical measure. In consequences to both issues, the extracted trend from SSA tended to flatten out and did not show any distinct pattern. This problem was approached in two ways. First, an Iterative Oblique SSA (Iterative O-SSA) was presented to make adjustment to the singular values data. Second, a measure was introduced to group the decomposed eigenvector based on Robust Sparse K-means (RSK-Means). As the results, the extracted trend using modification of SSA appeared to fit the original time series and looked more flexible compared to SSA.</p>

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Sanyal, Manas K., Indranil Ghosh, and R. K. Jana. "Characterization and Predictive Analysis of Volatile Financial Markets Using Detrended Fluctuation Analysis, Wavelet Decomposition, and Machine Learning." International Journal of Data Analytics 2, no. 1 (2021): 1–31. http://dx.doi.org/10.4018/ijda.2021010101.

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This paper proposes a granular framework for examining the dynamics of stock indexes that exhibit nonparametric and highly volatile behavior, and subsequently carries out the predictive analytics task by integrating detrended fluctuation analysis (DFA), maximal overlap discrete wavelet transformation (MODWT), and machine learning algorithms. DFA test ascertains the key temporal characteristics of the daily closing prices. MODWT decomposes the time series into granular components. Four pattern recognition algorithms—adaptive neuro fuzzy inference system (ANFIS), dynamic evolving neural-fuzzy inference system (DENFIS), bagging and deep belief network (DBN)—are then used on the decomposed components to obtain granular level forecasts. The entire exercise is performed on daily closing prices of Dow Jones Industrial Average (DJIA), National Stock Exchange of India (NIFTY), Karachi Stock Exchange (KSE), Taiwan Stock Exchange (TWSE), Financial Times Stock Exchange (FTSE), and German Stock Exchange (DAX). MODWT-Bagging and MODWT-DBN appear as superior forecasting models.

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Daza, Eric. "Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials*." Methods of Information in Medicine 57, S 01 (2018): e10-e21. http://dx.doi.org/10.3414/me16-02-0044.

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Summary Background: Many of an individual’s historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, regularly monitored clinical events or chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-effect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an N1OS association as a causal effect (e.g., as if randomization had occurred) is needed. Objectives: Our goal in this paper is to help bridge the methodological gap between risk-factor discovery and N1RT testing by introducing a basic counterfactual framework for N1OS design and personalized causal analysis. Methods and Results: We introduce and characterize what we call the average period treatment effect (APTE), i.e., the estimand of interest in an N1RT, and build an analytical framework around it that can accommodate autocorrelation and time trends in the outcome, effect carryover from previous treatment periods, and slow onset or decay of the effect. The APTE is loosely defined as a contrast (e.g., difference, ratio) of averages of potential outcomes the individual can theoretically experience under different treatment levels during a given treatment period. To illustrate the utility of our framework for APTE discovery and estimation, two common causal inference methods are specified within the N1OS context. We then apply the framework and methods to search for estimable and interpretable APTEs using six years of the author’s self-tracked weight and exercise data, and report both the preliminary findings and the challenges we faced in conducting N1OS causal discovery. Conclusions: Causal analysis of an individual’s time series data can be facilitated by an N1RT counterfactual framework. However, for inference to be valid, the veracity of certain key assumptions must be assessed critically, and the hypothesized causal models must be interpretable and meaningful.

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Dorndorf, Alexander, Boris Kargoll, Jens-André Paffenholz, and Hamza Alkhatib. "Bayesian Robust Multivariate Time Series Analysis in Nonlinear Models with Autoregressive and t-Distributed Errors." Engineering Proceedings 5, no. 1 (2021): 20. http://dx.doi.org/10.3390/engproc2021005020.

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Many geodetic measurement data can be modelled as a multivariate time series consisting of a deterministic (“functional”) model describing the trend, and a stochastic model of the correlated noise. These data are also often affected by outliers and their stochastic properties can vary significantly. The functional model of the time series is usually nonlinear regarding the trend parameters. To deal with these characteristics, a time series model, which can generally be explained as the additive combination of a multivariate, nonlinear regression model with multiple univariate, covariance-stationary autoregressive (AR) processes the white noise components of which obey independent, scaled t-distributions, was proposed by the authors in previous research papers. In this paper, we extend the aforementioned model to include prior knowledge regarding various model parameters, the information about which is often available in practical situations. We develop an algorithm based on Bayesian inference that provides a robust and reliable estimation of the functional parameters, the coefficients of the AR process and the parameters of the underlying t-distribution. We approximate the resulting posterior density using Markov chain Monte Carlo (MCMC) techniques consisting of a Metropolis-within-Gibbs algorithm.

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Di Persio, Luca, and Samuele Vettori. "Markov Switching Model Analysis of Implied Volatility for Market Indexes with Applications to S&P 500 and DAX." Journal of Mathematics 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/753852.

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We adopt aregime switchingapproach to study concrete financial time series with particular emphasis on their volatility characteristics considered in a space-time setting. In particular the volatility parameter is treated as an unobserved state variable whose value in time is given as the outcome of an unobserved, discrete-time and discrete-state, stochastic process represented by a suitable Markov chain. We will take into account two different approaches for inference on Markov switching models, namely, the classical approach based on the maximum likelihood techniques and the Bayesian inference method realized through a Gibbs sampling procedure. Then the classical approach shall be tested on data taken from theStandard & Poor’s 500and theDeutsche Aktien Indexseries of returns in different time periods. Computations are given for a four-state switching model and obtained numerical results are put beside by explanatory graphs which report the outcomes obtained exploiting both smoothing and filtering algorithms used in the estimation/calibration procedures we proposed to infer on the switching model parameters.

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Ziembińska, Paulina. "The importance of data revisions for statistical inference." Wiadomości Statystyczne. The Polish Statistician 66, no. 2 (2021): 7–24. http://dx.doi.org/10.5604/01.3001.0014.7387.

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The aim of the study is a quantitative analysis of revisions conducted by means of a new, real-time macroeconomic dataset for Poland, designed on the basis of the Statistical bulletin (Biuletyn statystyczny) published by Statistics Poland, covering the period from as early as 1995 until 2017. Polish data have positively verified a number of hypotheses concerning the impact of data revisions on the modelling process. Procedures assessing the properties of time series can yield widely discrepant results, depending on the extent to which the applied data have been revised. A comparison of the fitted ARIMA models for series of initial and final data demonstrates that the fitted models are similar for the majority of variables. In the cases where the form of the model is identical for both series, the coefficients retain their scale and sign. Most differences between coefficients result from a different structure of the fitted model, which causes differences in the autoregressive structure and can have a considerable impact on the ex ante inference. A prognostic experiment confirmed these observations. For a large number of variables, the total impact of revisions on the forecasting process exceeds 10%. Extreme cases, where the impact goes beyond 100%, or situations where data have a direct impact on the forecast sign, are also relatively frequent. Taking these results into account by forecasters could significantly improve the quality of their predictions. The forecast horizon has a minor impact on these conclusions. The article is a continuation of the author's work from 2017.

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Diniz, Marcio A., Carlos A. B. Pereira, and Julio M. Stern. "Cointegration and Unit Root Tests: A Fully Bayesian Approach." Entropy 22, no. 9 (2020): 968. http://dx.doi.org/10.3390/e22090968.

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To perform statistical inference for time series, one should be able to assess if they present deterministic or stochastic trends. For univariate analysis, one way to detect stochastic trends is to test if the series has unit roots, and for multivariate studies it is often relevant to search for stationary linear relationships between the series, or if they cointegrate. The main goal of this article is to briefly review the shortcomings of unit root and cointegration tests proposed by the Bayesian approach of statistical inference and to show how they can be overcome by the Full Bayesian Significance Test (FBST), a procedure designed to test sharp or precise hypothesis. We will compare its performance with the most used frequentist alternatives, namely, the Augmented Dickey–Fuller for unit roots and the maximum eigenvalue test for cointegration.

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Alekseev, Valery I. "Forecasting the orbital-climate diagram dynamics based on the wavelet analysis and fuzzy neural networks." Yugra State University Bulletin 14, no. 1 (2018): 13–21. http://dx.doi.org/10.17816/byusu20180113-21.

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Based on the new concept of the V. Bolshakov's orbital theory of paleoclimate, as well as the multiscale time-series wavelet decomposition method, and neural network fuzzy inference rules, the paper derives a predicted curve for the so-called orbital-climate diagram (OCD) in the ratio of (eccentricity, orbit inclination, precession) within the time interval from -1000 kyr in the past to 100 kyr in the future since modern times. This diagram features the Earth climate change caused by an insolation change to be the principal factor of the climate change, driven by Earth's orbital elements changes. Efficiency of the time-series forecast method is proved by the obtained predicted OCD trajectory verification within the past 100 kyr period and other paleoclimate data with a correlation coefficient 0.93.

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Demanuele, Charmaine, Peter Kirsch, Christine Esslinger, Mathias Zink, Andreas Meyer-Lindenberg, and Daniel Durstewitz. "Area-Specific Information Processing in Prefrontal Cortex during a Probabilistic Inference Task: A Multivariate fMRI BOLD Time Series Analysis." PLOS ONE 10, no. 8 (2015): e0135424. http://dx.doi.org/10.1371/journal.pone.0135424.

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Dvorkina, Tatiana, Olga Kunyavskaya, Andrey V. Bzikadze, Ivan Alexandrov, and Pavel A. Pevzner. "CentromereArchitect: inference and analysis of the architecture of centromeres." Bioinformatics 37, Supplement_1 (2021): i196—i204. http://dx.doi.org/10.1093/bioinformatics/btab265.

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Abstract Motivation Recent advances in long-read sequencing technologies led to rapid progress in centromere assembly in the last year and, for the first time, opened a possibility to address the long-standing questions about the architecture and evolution of human centromeres. However, since these advances have not been yet accompanied by the development of the centromere-specific bioinformatics algorithms, even the fundamental questions (e.g. centromere annotation by deriving the complete set of human monomers and high-order repeats), let alone more complex questions (e.g. explaining how monomers and high-order repeats evolved) about human centromeres remain open. Moreover, even though there was a four-decade-long series of studies aimed at cataloging all human monomers and high-order repeats, the rigorous algorithmic definitions of these concepts are still lacking. Thus, the development of a centromere annotation tool is a prerequisite for follow-up personalized biomedical studies of centromeres across the human population and evolutionary studies of centromeres across various species. Results We describe the CentromereArchitect, the first tool for the centromere annotation in a newly sequenced genome, apply it to the recently generated complete assembly of a human genome by the Telomere-to-Telomere consortium, generate the complete set of human monomers and high-order repeats for ‘live’ centromeres, and reveal a vast set of hybrid monomers that may represent the focal points of centromere evolution. Availability and implementation CentromereArchitect is publicly available on https://github.com/ablab/stringdecomposer/tree/ismb2021 Supplementary information Supplementary data are available at Bioinformatics online.

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Baumgartner, Michael, and Alrik Thiem. "Often Trusted but Never (Properly) Tested: Evaluating Qualitative Comparative Analysis." Sociological Methods & Research 49, no. 2 (2017): 279–311. http://dx.doi.org/10.1177/0049124117701487.

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To date, hundreds of researchers have employed the method of Qualitative Comparative Analysis (QCA) for the purpose of causal inference. In a recent series of simulation studies, however, several authors have questioned the correctness of QCA in this connection. Some prominent representatives of the method have replied in turn that simulations with artificial data are unsuited for assessing QCA. We take issue with either position in this impasse. On the one hand, we argue that data-driven evaluations of the correctness of a procedure of causal inference require artificial data. On the other hand, we prove all previous attempts in this direction to have been defective. For the first time in the literature on configurational comparative methods, we lay out a set of formal criteria for an adequate evaluation of QCA before implementing a battery of inverse-search trials to test how this method performs in different recovery contexts according to these criteria. While our results indicate that QCA is correct when generating the parsimonious solution type, they also demonstrate that the method is incorrect when generating the conservative and intermediate solution type. In consequence, researchers using QCA for causal inference, particularly in human-sensitive areas such as public health and medicine, should immediately discontinue employing the method’s conservative and intermediate search strategies.

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Journal articles on the topic 'Time-series analysis Inference'

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