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PubMedDate not supplied

[Mathematical models in epidemiological analysis. II. Typhoid fever: time series analysis (author's transl)].

In this paper the systematic analysis of oro-faecal infectious diseases is continued. In order to propose a dynamic model for the evolution of the endemic typhoid fever, we have studied the behaviour of the number of cases of this disease in the town of Bari (Italy) during the years 1955-1978, by the methods of time series analysis. The analysis has pointed out the typical seasonal character of such a disease, and in particular the presence of a main winter cycle followed by a less evident summer cycle. The stochastic model which has been identified on the basis of the data from 1964 to 1978 allows a forecast for the years after 1978.

Epidemiologic Methods↗

PubMed1990

The treatment of tension headache by acupuncture: a controlled single case design with time series analysis.

A single case design, with time series analysis, was employed to evaluate the efficacy of acupuncture in the treatment of tension headache. Fourteen patients were given eight weekly treatments, four of true acupuncture and four of sham in random order. Mean pain in medication scores were reduced by 52% and 54% respectively at initial follow-up. Reductions in pain scores of over 50% were achieved by half the patients and the significance of these changes confirmed by time series analysis. The majority of patients maintained their gains at four month follow-up. True acupuncture was shown to be significantly superior to sham, demonstrating a specific therapeutic action, in four patients. In the remainder no difference was observed. Possible mechanisms for these effects are discussed. Acupuncture is a potentially valuable treatment for tension headache but further research is needed.

Acupuncture Analgesia↗

PubMed2001

Prediction of physical and chemical qualities of crude-oil using statistical time series analysis.

An application of a statistical time series analysis method is given for the interpretation of physical and chemical qualities of crude-oil samples taken from two oil-fields in Poland. The analytical data was gathered in the years 1995-1999, the information being collected once a month. Randomly selected crude-oil samples were examined in several analytical laboratories. For each sample examined, the following chemical-physical parameters were defined: date, source of the sample, specific gravity, density, colour, relative viscosity, viscosity, kinematic viscosity, drip point, setting point, etc. The data obtained played a decisive role in classifying the samples of crude-oil and in forecasting the properties of crude-oil that will be obtained from the mines in the future. To process the data obtained, the STATISTICA programme was used. It contains a module for time series analysis and for graphical presentation of the results.

Journal Article↗

PubMed1993

An introduction to multiple time series analysis.

An expository account of multiple time series analysis is presented. Modeling several related time series together makes it possible to ascertain dynamic leading, lagging, and feedback relationships among the series; to produce more efficient forecasts and, in some situations; to develop control schemes. Procedures for building linear vector autoregressive moving average models are sketched and illustrated. An extension to parallel vector time series in a hierarchical framework is given.

Health Services Research↗

PubMed1991

Time series analysis of knowledge of results effects during motor skill acquisition.

Time series analysis was used to investigate the hypothesis that during acquisition of a motor skill, knowledge of results (KR) information is used to generate a stable internal referent about which response errors are randomly distributed. Sixteen subjects completed 50 acquisition trials of each of three movements whose spatial-temporal characteristics differed. Acquisition trials were either blocked, with each movement being presented in series, or randomized, with the presentation of movements occurring in random order. Analysis of movement time data indicated the contextual interference effect reported in previous studies was replicated in the present experiment. Time series analysis of the acquisition trial data revealed the majority of individual subject response patterns during blocked trials were best described by a model with a temporarily stationary, internal reference of the criterion and systematic, trial-to-trial variation of response errors. During random trial conditions, response patterns were usually best described by a "White-noise" model. This model predicts a permanently stationary, internal reference associated with randomly distributed response errors that are unaffected by KR information. These results are not consistent with previous work using time series analysis to describe motor behavior (Spray & Newell, 1986).

Adolescent↗

PubMed1995

[A methodological review of specific techniques of time series analysis in epidemiology and public health].

Time series analysis of health indicator can contribute to the improvement of knowledge in epidemiology and public health. It is necessary to use models and methods appropriate to the time domain. A class of models proposed by Box & Jenkins are presented together with the necessary tools for identifying such models. Two useful generalisations are also discussed. To study the link between two time series, regression models need to include autocorrelated error terms while to evaluate the effect of an intervention, the use of intervention models are appropriate. This paper presents basic concepts of such analyses in a simple way to encourage the use of those methods.

Analysis of Variance↗

PubMed1982

A simplified time-series analysis for evaluating treatment interventions.

Time-series analysis procedures for analyzing behavior data are receiving increasing support. However, several authorities strongly recommend using at least 50-100 points per experimental phase. A complex mathematical model must then be empirically developed using computer programs to extract serial dependency from the data before the effects of treatment interventions can be evaluated. The present discussion provides a simple method of evaluating intervention effects that can be used with as few as 8 points per experimental phase. The calculations are easy enough to do by hand.

Behavior Therapy↗

PubMed2001

Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains.

Even in the absence of an experimental effect, functional magnetic resonance imaging (fMRI) time series generally demonstrate serial dependence. This colored noise or endogenous autocorrelation typically has disproportionate spectral power at low frequencies, i.e., its spectrum is (1/f)-like. Various pre-whitening and pre-coloring strategies have been proposed to make valid inference on standardised test statistics estimated by time series regression in this context of residually autocorrelated errors. Here we introduce a new method based on random permutation after orthogonal transformation of the observed time series to the wavelet domain. This scheme exploits the general whitening or decorrelating property of the discrete wavelet transform and is implemented using a Daubechies wavelet with four vanishing moments to ensure exchangeability of wavelet coefficients within each scale of decomposition. For (1/f)-like or fractal noises, e.g., realisations of fractional Brownian motion (fBm) parameterised by Hurst exponent 0 < H < 1, this resampling algorithm exactly preserves wavelet-based estimates of the second order stochastic properties of the (possibly nonstationary) time series. Performance of the method is assessed empirically using (1/f)-like noise simulated by multiple physical relaxation processes, and experimental fMRI data. Nominal type 1 error control in brain activation mapping is demonstrated by analysis of 13 images acquired under null or resting conditions. Compared to autoregressive pre-whitening methods for computational inference, a key advantage of wavelet resampling seems to be its robustness in activation mapping of experimental fMRI data acquired at 3 Tesla field strength. We conclude that wavelet resampling may be a generally useful method for inference on naturally complex time series.

Artifacts↗

PubMed1991

Environmetric time-series analysis: modelling natural systems from experimental time-series data.

Using the modelling of solute transport in flowing media as an example, this paper outlines the main aspects of a systematic approach to the modelling of natural systems from experimental time-series data. The objective of the approach, which exploits sophisticated methods of recursive parameter estimation, is to produce a parametrically efficient, data-based model which is both physically meaningful and statistically well defined. Although the proposed methodology has its origins in systems and control theory and may be unfamiliar to some natural scientists, it has been developed and refined for use with natural environmental systems over the past 20 years, and has wide application potential in areas such as biology and ecology. In this sense, the paper is intended to introduce the more general reader to the topic, in the hope that the tutorial review and practical examples will stimulate interest and encourage reference to the many publications cited in the paper. The practical examples are concerned with the modelling of pollutant dispersion in stream channels: phloem translocation and carbon partitioning in plants: and rainfall-streamflow modelling in a river catchment.

Algorithms↗

PubMed2005

Hierarchical structure of the energy landscape of proteins revisited by time series analysis. II. Investigation of explicit solvent effects.

Time series analysis tools are employed on the principal modes obtained from the C(alpha) trajectories from two independent molecular-dynamics simulations of alpha-amylase inhibitor (tendamistat). Fluctuations inside an energy minimum (intraminimum motions), transitions between minima (interminimum motions), and relaxations in different hierarchical energy levels are investigated and compared with those encountered in vacuum by using different sampling window sizes and intervals. The low-frequency low-indexed mode relationship, established in vacuum, is also encountered in water, which shows the reliability of the important dynamics information offered by principal components analysis in water. It has been shown that examining a short data collection period (100 ps) may result in a high population of overdamped modes, while some of the low-frequency oscillations (<10 cm(-1)) can be captured in water by using a longer data collection period (1200 ps). Simultaneous analysis of short and long sampling window sizes gives the following picture of the effect of water on protein dynamics. Water makes the protein lose its memory: future conformations are less dependent on previous conformations due to the lowering of energy barriers in hierarchical levels of the energy landscape. In short-time dynamics (<10 ps), damping factors extracted from time series model parameters are lowered. For tendamistat, the friction coefficient in the Langevin equation is found to be around 40-60 cm(-1) for the low-indexed modes, compatible with literature. The fact that water has increased the friction and that on the other hand has lubrication effect at first sight contradicts. However, this comes about because water enhances the transitions between minima and forces the protein to reduce its already inherent inability to maintain oscillations observed in vacuum. Some of the frequencies lower than 10 cm(-1) are found to be overdamped, while those higher than 20 cm(-1) are slightly increased. As for the long-time dynamics in water, it is found that random-walk motion is maintained for approximately 200 ps (about five times of that in vacuum) in the low-indexed modes, showing the lowering of energy barriers between the higher-level minima.

Computer Simulation↗

PubMed2004

Ecogeographic patterns of rabies in southern Ontario based on time series analysis.

We describe a method based on time series analysis that divided the rabies enzootic area of southern Ontario into 13 regions using data collected at the township level, the smallest available geographical unit for Ontario (Canada). The intent was to discover ecogeographic patterns if such existed. For the period 1957-89, the quarterly time series of fox rabies cases for each of the 423 townships in the study area was correlated with the time series of its adjacent neighbors. Townships were then linked to adjacent townships provided the pair-wise correlations had significant correlation coefficients. This procedure produced 13 clusters that remained stable when additional lead/lag relationships between townships were examined. Furthermore, those clusters, which we then termed "rabies units," had different behaviors in terms of species distribution, persistence, and periodicity. Time series in adjacent units were not synchronous. We discuss how our findings influenced the rabies control program in Ontario, how they relate to recent findings about the distribution of fox rabies virus subtypes, and how they lend support for the role of metapopulation structulre in persistence of disease.

Animals↗

PubMed1994

Time series analysis of physiological response during ICU visitation.

Time series analysis (TSA) is an important statistical procedure for clinical nursing research. The current paucity of nursing research reports using TSA may be due to unfamiliarity with this technique. In this article, TSA is compared with the ordinary least squares regression model; validity concerns of time series designs are discussed; and concomitant and interrupted TSA of data collected on the effects of family visitation on intracranial pressure (ICP), heart rate, and blood pressure of patients in ICUs are presented. The concomitant TSA of the effect of family on ICP suggested that family presence tended to be associated with decreased ICP. Interrupted TSA indicated the effect of family on heart rate and blood pressure was not as consistent: The overall effect on blood pressure appeared to be negligible, and heart rate may increase overall. Restrictive visiting policies, once typical of intensive care units, should be reconsidered.

Blood Pressure↗

PubMed2006

Identification of statistical patterns in complex systems via symbolic time series analysis.

Identification of statistical patterns from observed time series of spatially distributed sensor data is critical for performance monitoring and decision making in human-engineered complex systems, such as electric power generation, petrochemical, and networked transportation. This paper presents an information-theoretic approach to identification of statistical patterns in such systems, where the main objective is to enhance structural integrity and operation reliability. The core concept of pattern identification is built upon the principles of Symbolic Dynamics, Automata Theory, and Information Theory. To this end, a symbolic time series analysis method has been formulated and experimentally validated on a special-purpose test apparatus that is designed for data acquisition and real-time analysis of fatigue damage in polycrystalline alloys.

Artificial Intelligence↗

PubMed2006

Testing statistical significance of multivariate time series analysis techniques for epileptic seizure prediction.

Nonlinear time series analysis techniques have been proposed to detect changes in the electroencephalography dynamics prior to epileptic seizures. Their applicability in practice to predict seizure onsets is hampered by the present lack of generally accepted standards to assess their performance. We propose an analytic approach to judge the prediction performance of multivariate seizure prediction methods. Statistical tests are introduced to assess patient individual results, taking into account that prediction methods are applied to multiple time series and several seizures. Their performance is illustrated utilizing a bivariate seizure prediction method based on synchronization theory.

Algorithms↗

PubMed1984

Oscillatory fluctuations in the incidence of infectious disease and the impact of vaccination: time series analysis.

This paper uses the techniques of time series analysis (autocorrelation and spectral analysis) to examine oscillatory secular trends in the incidence of infectious diseases and the impact of mass vaccination programmes on these well-documented phenomena. We focus on three common childhood diseases: pertussis and mumps (using published disease-incidence data for England and Wales) and measles (using data from England and Wales, Scotland, North America and France). Our analysis indicates highly statistically significant seasonal and longer-term cycles in disease incidence in the prevaccination era. In general, the longer-term fluctuations (a 2-year period for measles, 3-year periods for pertussis and mumps) account for most of the cyclical variability in these data, particularly in the highly regular measles series for England and Wales. After vaccination, the periods of the longer-term oscillations tend to increase, an observation which corroborates theoretical predictions. Mass immunization against measles (which reduces epidemic fluctuations) magnifies the relative importance of the seasonal cycles. By contrast, we show that high levels of vaccination against whooping cough in England and Wales appear to have suppressed the annual cycle.

Biometry↗

PubMed2006

On the use of bipolar montages for time-series analysis of intracranial electroencephalograms.

OBJECTIVE: Bipolar montages are routinely employed for the interpretation of scalp and intracranial EEGs (icEEGs). In this manuscript we consider the assumptions that support the use of a bipolar montage and question the universal appropriateness of bipolar representation of icEEGs for the time-series analysis of these signals. Bipolar montages introduce an element of spatial processing into the observed time-series. In the case of icEEGs, we argue ambiguity may be introduced in some settings through this operation because of a lack of certifiability of local differentiability and continuity of the spatial structure of icEEGs, and their suboptimal spatial sampling. METHODS: Example icEEGs were collected from three patients being studied for possible resective epilepsy surgery. Referential and bipolar representations of these signals were subjected to different visual and time-series analysis. The time-series measures calculated were the power spectral density and magnitude squared coherence. RESULTS: Visual analysis and time-series measures revealed that the icEEG time-series was altered by the use of a bipolar montage. The changes resulted from either the introduction of unrelated information from the two referential time-series into the bipolar time-series, or from the removal or alteration of information common to the two referential time-series in the bipolar time-series. The changes could not be predicted without prior knowledge of the relationship between measurement sites that form the bipolar montage. CONCLUSIONS: In certain settings, bipolar montages alter icEEGs and can confound the time-series analysis of these signals. In such settings, bipolar montages should be used with caution in the time-series analysis of icEEGs. SIGNIFICANCE: This manuscript addresses the representation of the intracranial EEG for time-series analysis. There may be contexts where the assumptions underpinning correct application of the bipolar montage to the intracranial EEG are not satisfied.

Brain↗

PubMed1993

An hypothesis for the periodicity of smallpox epidemics as revealed by time series analysis.

Parish registers have been studied by time series analysis to detect smallpox epidemics in England during 1600-1800. Confirmatory evidence was provided by the seasonality of child mortality. A 5-year cycle in smallpox epidemics was detected in medium-sized, rural towns. Consideration of the mathematics of the dynamics of viral diseases suggests that the true interepidemic period where smallpox is endemic should be 2-3 years and it is concluded that, in the towns studied, the disease was not endemic but that the oscillations were established by 5-year cycles of periods of famine associated with high wheat prices. The cross-correlation function between the two cycles shows zero lag and the input-output function shows significant coherence. Another epidemic follows only when a sufficient density of susceptibles has been established by births, so that the cycles become phase-locked. It is predicted that smallpox (i) was endemic in London and other large cities, with 2-3 year epidemics, (ii) was epidemic with a 5-year oscillation in rural towns, (iii) did not reach epidemic proportions in scattered communities.

Birth Rate↗

PubMed1980

Biofeedback of slow cortical potentials. II. Analysis of single event-related slow potentials by time series analysis.

A single trial analysis of slow cortical potentials treating the EEG as a time series was developed. The method was applied to data resulting from an experiment on self-regulation of slow cortical potentials (SCP). Parametric models of the EEG were developed on the basis of autoregressive filter models and a two-component model of SCP (during 6 sec intervals), taking into account ocular influences as a further parameter. Results from different models were compared with each other and with results of averaging SCP data. For the present data time series analysis and traditional analysis provided qualitatively equal results, but fewer trials were necessary for analysis in the single trial approach and more detailed structures of the data became evident. If the EEG was filtered above 5 Hz it could be described by an autoregressive filter model of low order. Ocular influences were estimated as too small in a non-filtered EEG compared to the filtered EEG.

Biofeedback, Psychology↗