Application Of Time Series Analysis Research Articles

Residual autocorrelation testing for vector error correction models

In applied time series analysis, checking for autocorrelation in a fitted model is a routine diagnostic tool. Therefore it is useful to know the asymptotic and small sample properties of the standard tests for the case when some of the variables are cointegrated. The properties of residual autocorrelations of vector error correction models (VECMs) and tests for residual autocorrelation are derived. In particular, the asymptotic distributions of Lagrange multiplier (LM) and portmanteau tests are given. Monte Carlo simulations show that the LM tests have satisfactory size properties only if autocorrelation of small order is tested in systems of small dimension. In contrast, portmanteau tests have roughly correct size in small samples only if higher order residual autocorrelation is tested. Their critical values have to be adjusted for the cointegration rank of the system, however.

Seasonal cycles and persistence in an acarine predator‐prey system on cassava in Africa

AbstractWe applied time series analysis and a mechanistic predator‐prey model to long‐term data of monthly population counts of the herbivorous pest mite Mononychellus tanajoa and its introduced phytoseiid predator Typhlodromalus aripo from a cassava field in Benin, West Africa. In this approach, we determined the extent to which the main features of the observed predator‐prey fluctuations in cassava fields can be explained from biotic traits inherent to the biology of predator and prey, and the extent of the significance of abiotic factors in determining population levels. The time series analyses with cross‐correlation showed that the period of predator‐prey fluctuations coincided with the annual pattern of intense rainfall and onset of dry season. A pronounced M. tanajoa peak followed after a short lag (2 weeks) by a T. aripo peak coincided with a trough in rainfall intensity. Both the prey and predator had local and lower peaks that coincided with high rainfall intensity, but with a considerably longer lag (ca. 3 months) compared with the high peaks occurring at the onset of the dry season. Regression of log‐transformed data series (over a 7‐year period) showed that—except for the first year after predator release—M. tanajoa fluctuated around an almost time‐invariant mean population density, while T. aripo densities showed a consistent decline over the full observation period. To explain observed trends and periodic components in the data‐series of predator and prey densities, we review hypotheses that are based on (1) the annual patterns and trends in abiotic factors, (2) mechanisms endogenous to the predator‐prey system and (3) a combination of exogenous and endogenous factors.

Time series modeling for evaluation of groundwater discharge rates into an urban subway system

This study investigates the application of time series analysis for evaluating groundwater discharge rates into a subway system in Seoul, Korea. A time series of daily precipitation and groundwater discharge rates observed at three subway stations— Gireum, Garibong and Sadang station—in 2001 were used for the time series modeling. Correlation analyses show that groundwater discharge rates are auto-correlated and correlated with precipitation. Based on these correlation analyses, the transfer function noise (TFN) models including the Box-Jenkins (BJ) autoregressive moving average (ARMA) model were applied to evaluate the groundwater discharge rates into a subway tunnel when precipitation data is given. As a result, the TFN model is acceptable at all three stations. Though the analyses are limited to regularly observed time series data and a linear groundwater system, this study provides a practical application of time series models for groundwater discharge management in the subway system. In particular, for the Seoul subway system where the rapid deterioration of water resources occurs, the estimation of groundwater discharge rates is very important for an efficient management of the water resources in the urban environment.

Application of time-series analyses to the hydrological functioning of an Alpine karstic system: the case of Bange-L’Eau-Morte

Abstract. This paper analyses the hydrological functioning of the Bange-L’Eau-Morte karstic system using classical and original techniques, recession curves, correlation and spectral analyses, noise analysis and wavelet analyses. The main characteristics that can be deduced are the recession coefficients, the dynamic volume of storage, the response time of the system, the quickflow and baseflow components and the snowmelt characteristics. The non-stationary and timescale-dependent behaviour of the system is studied and particular features of the runoff are shown. The step-by-step use of these different techniques provides a general methodology applicable to different karstic systems to provide quantifiable and objective criteria for differentiation and comparison of karstic systems. Keywords: karstic hydrology, Bauges mountains, recession curves, correlation and spectral analysis, wavelet analysis, snowmelt

Application of time series analysis on molecular dynamics simulations of proteins: a study of different conformational spaces by principal component analysis.

Time series analysis is applied on the collective coordinates obtained from principal component analysis of independent molecular dynamics simulations of alpha-amylase inhibitor tendamistat and immunity protein of colicin E7 based on the Calpha coordinates history. Even though the principal component directions obtained for each run are considerably different, the dynamics information obtained from these runs are surprisingly similar in terms of time series models and parameters. There are two main differences in the dynamics of the two proteins: the higher density of low frequencies and the larger step sizes for the interminima motions of colicin E7 than those of alpha-amylase inhibitor, which may be attributed to the higher number of residues of colicin E7 and/or the structural differences of the two proteins. The cumulative density function of the low frequencies in each run conforms to the expectations from the normal mode analysis. When different runs of alpha-amylase inhibitor are projected on the same set of eigenvectors, it is found that principal components obtained from a certain conformational region of a protein has a moderate explanation power in other conformational regions and the local minima are similar to a certain extent, while the height of the energy barriers in between the minima significantly change. As a final remark, time series analysis tools are further exploited in this study with the motive of explaining the equilibrium fluctuations of proteins.

Time series analysis of long-term data sets of atmospheric mercury concentrations.

Different aspects and techniques of time series analysis were used to investigate long-term data sets of atmospheric mercury in the Northern Hemisphere. Two perennial time series from different latitudes with different seasonal behaviour were chosen: first, Mace Head on the west coast of Ireland (53 degrees 20'N, 9 degrees 54'W), representing Northern Hemispherical background conditions in Europe with no indications for so-called atmospheric mercury depletion events (AMDEs); and second, Alert, Canada (82 degrees 28'N, 62 degrees 30'W), showing strong AMDEs during Arctic springtime. Possible trends were extracted and forecasts were performed by using seasonal decomposition procedures, autoregressive integrated moving average (ARIMA) methods and exponential smoothing (ES) techniques. The application of time series analysis to environmental data is shown in respect of atmospheric long-term data sets, and selected advantages are discussed. Both time series have not shown any statistically significant temporal trend in the gaseous elemental mercury (GEM) concentrations since 1995, representing low Northern Hemispherical background concentrations of 1.72+/-0.09 ng m(-3) (Mace Head) and 1.55+/-0.18 ng m(-3) (Alert), respectively. The annual forecasts for the GEM concentrations in 2001 at Alert by two different techniques were in good agreement with the measured concentrations for this year.

Use of time series analysis for the identification of tidal effect on groundwater in the coastal area of Kimje, Korea

The purpose of this study is to apply time series analysis to investigate whether the groundwater quality in the coastal area is affected by the tide. Continuous and regular in situ monitoring data of electrical conductivity (EC) and groundwater level, and tidal level data measured by the National Oceanographic Research Institute were used for the time series analysis. Through the time series analysis, it is known that EC and groundwater level conspicuously fluctuate with two periodicities (15.4 and 0.52-day), which is very similar to those of the tide. Also the behaviors of their fluctuations vary in accordance with the tidal period. These indicate that the groundwater quality has been mainly controlled by the tidal level, and the strength of tidal effect on the groundwater quality is different according to the tidal period.

The psychophysiology of the maternal-fetal relationship.

The enigmatic quality of the maternal-fetal relationship has been extolled throughout history with little empirical support. We apply time series analysis to data for 137 maternal-fetal pairs collected at 20, 24, 28, 32, 36, and 38 weeks gestation. Maternal heart rate and skin conductance data were digitized in tandem with fetal heart rate and motor activity. No temporal relations between fetal heart rate and either maternal variable were found, although averaged maternal and fetal heart rates were correlated from 32 weeks. Consistent temporal associations between fetal movement and maternal heart rate and skin conductance were detected. Fetal movement stimulated rises in each parameter, peaking at 2 and 3 s, respectively. Associations did not change over gestation, were unaffected by a maternal stressor, and showed within-pair stability. The bidirectional nature of the maternal-fetal relationship is considered.

Relationships between dissolved and particulate carbohydrate temporal patterns and mucilaginous aggregates formation: evidences from time series analysis and biochemical composition of aggregates

A 3-year monitoring of dissolved and particulate carbohydrate concentrations in four transects located in the Adriatic and Tyrrhenian Seas was performed in order to get information on the role played by carbohydrates in the mucilage formation in these areas. The application of time series analysis pointed out that the concentration of dissolved carbohydrates does not vary significantly in coincidence of the mucilage appearance showing an almost constant state with respect to time. In contrast, wider temporal variations of carbohydrate amounts, either increase or decrease, were observed when mucilages were lacking or reduced. This almost constant state of carbohydrate amounts observed in presence of mucilages that we define ‘steady state’ could be associated to an alteration of the complex chemical equilibrium between synthesis and degradation (either hydrolysis or oxidation) reactions of the organic polymers which are typical of the humification processes in the marine environment. The results of this study suggest that the monitoring of carbohydrates can represent an useful tool for the comprehension of the most relevant phenomena of mucilage appearance in the Northern Adriatic Sea.

A time series approach to g convergence

This paper applies time series analysis to study how the gap between a number of countries and the USA evolves through time. As other authors, it is found that time series analysis provides a better insight into the concept of convergence than the cross sectional one. The econometric results show that the stochastic behaviour of the output disparity varies considerably: neither the steady-state equilibrium nor the speed of convergence are unique and constant across countries and time. In general, there is catching up for European countries, convergence for East and South Asian countries, and neither of them for Latin American countries.

Time-series analyses of mandibular and perioral soft tissue movements during mastication.

Masticatory movements are rhythmically repeated and coordinated movements of the jaw, tongue and facial muscles. Thus, we considered that the elucidation of movements that are specific to perioral soft tissue, as a result of perioral facial muscle activities, should be useful for evaluation of the smoothness of masticatory movements. The aim of this study was to evaluate the smoothness of masticatory movements from the component of movements that are specific to perioral soft tissue during mastication by the application of time-series analysis. The subjects were 15 healthy persons with complete natural dentition. The experimental food used for mastication in this study was sufficiently softened chewing gum. The results showed that the component of movements that are specific to perioral soft tissue during mastication are the equal repetition spatially and stable movements temporally, and that these movements have the same accurate rhythm as that of mandibular movements and cooperate with mandibular movements temporally. Moreover, the results suggested, from the viewpoint of kinematics, that the innervation of the central pattern generator was concerned with the neural basis of rhythm generation of perioral facial muscles. Therefore, the component of movements that are specific to perioral soft tissue during mastication is useful for evaluation of the smoothness of masticatory movements.

A behavioral analysis of interest rates in Euro area

Analyses the structural behaviour of money and capital market interest rates 1999‐2001 in the European Monetary Union. Finds that the positive correlations between interest rates of different time periods get stronger as the time periods get closer, derives the principal components which explain most of the variability and applies time series analysis to the model to produce forecasts which come very close to actual values. Develops a model of the Down Jones EURO STOXX financial sector index which also shows reliable forecasting power except for the Feb 2001 period when stock markets were “in turmoil”. Compares the returns for one‐month holdings of zero‐coupon European government bonds with different maturities for various return, risk and risk‐return measures; and finds that a one‐month holding of a two‐year bond is the best investment.

Dissolved Oxygen Balance Model for Neris

We consider a dissolved oxygen balance model for Neris, which includes biochemical oxygen demand, nitrification, sedimentation, algae respiration and photosynthesis. The load from point sources, tributaries and distributed sources are taken into account. Long-term systematic components such as drift and seasonal components are analysed by applying time series analysis.
 The model is adapted according to the State Environmental Monitoring, and source data of controlled pollution covering the period 1978-1998.

Making sense of antimicrobial use and resistance surveillance data: application of ARIMA and transfer function models

Making sense of antimicrobial use and resistance surveillance data: application of ARIMA and transfer function models

Forecasting Spring Wheat Yield Using Time Series Analysis

Techniques commonly used for wheat (Triticum aestivum L.) yield estimation employ weather data over the growing season. However, yield estimates are also required before wheat is sown—particularly by the grain‐exporting agencies to help them determine, in advance, wheat‐export targets. In that case, time series techniques relying on past yield data can be used for yield forecasting. In this paper, a procedure for applying time series analysis to forecast yield is described. A few techniques (linear trend, quadratic trend, simple exponential smoothing, double exponential smoothing, simple moving averaging, and double moving averaging) were tested to model the average spring wheat yield series for Saskatchewan, Canada. Using 1975–1993, 1975–1994, and 1975–1995 spring wheat yield data, yields were forecasted for 1994, 1995, and 1996, respectively. Based on a deterministic measure (i.e., mean squared error, MSE), it was found that the quadratic model produced the most accurate forecast during the model development periods (1975–1993, 1975–1994, and 1975–1995) and model testing periods (1994, 1995, and 1996). Further, a discussion is provided on improving the forecast by forecasting the yield for the homogeneous subareas (within Saskatchewan) instead for the entire Saskatchewan as a unit. The subareas could be constructed on the basis of soil‐climatic conditions or yield fluctuation, using a geographic information system.

LINKING ECOLOGICAL PATTERNS TO ENVIRONMENTAL FORCING VIA NONLINEAR TIME SERIES MODELS

The identification of key environmental forcings responsible for population patterns is a pervasive ecological problem and an important application of time series analysis. A common approach, implemented with methods such as cross-correlation and cross-spectral analysis, relies on matching scales of variability. This approach concludes that a population pattern is caused by a physical factor if their variances share a dominant period. In a nonlinear system, however, forcing at one temporal period can produce a response with variability at one or more different periods. Thus, scale-matching methods will be most successful at establishing cause-effect relationships in linear systems, or close to equilibria, where nonlinear systems are well approximated by linear ones. Here, we propose an alternative approach that does not assume linearity and relies on time series models that are both nonlinear and nonparametric. We specifically apply these models to determine the correct but unknown frequency of a periodic forcing. The time series are generated by simulation of a predator-prey model. Under periodic forcing, this type of model is known to be capable of different dynamic regimes, including chaos and quasi- periodicity, in which the power spectra of population numbers exhibit variance at fre- quencies other than that of the forcing. We show that nonlinear time series models, built with feedforward neural networks, are able to distinguish the correct forcing period in the predator-prey simulations. These results hold under two common limitations of ecological data: the presence of dynamical and measurement noise, and the availability of time series data for only one variable. We discuss future applications of the approach to more general environmental forcings, other than periodic.

Linear Processes, Long-Range Dependence and Asymptotic Expansions

Linear processes have a wide range of applications in time series analysis. This paper reviews some recent results by the author on the limit theory for the functionals of linear processes.

Forecasting surgical groups' total hours of elective cases for allocation of block time: application of time series analysis to operating room management.

Allocation of the correct amount of operating room (OR) "block time" can provide surgeons with access to sufficient OR time to complete their elective cases while optimally matching staffing with the elective case workload (to maximize labor productivity). To evaluate how to predict accurately total hours of elective cases performed by a surgical group using data from surgical services information systems, the authors addressed the following questions: (1) How many previous 4-week periods of data should be used to minimize error in forecasting a surgical group's total hours of elective cases? (2) Using the number of 4-week periods from question #1, can we detect trends or correlations between successive periods that could be used to improve forecasting accuracy? (3) How can results from questions #1 and #2 be used to calculate an upper prediction bound (upper limit) for the total hours of elective cases that will be completed in a future period? Prediction bounds can be used to budget staffing accurately. Time series analysis was performed on total hours of elective cases over 39 consecutive 4-week periods from 17 surgical groups. The average of 12 consecutive periods' total hours of elective cases had an appropriate error profile. The observations within each series of 12 consecutive 4-week periods followed a normal distribution, with each observation of total hours of elective cases not correlated with the subsequent observation. The average of the most recent 12 4-week periods can be used to predict surgical groups' future use of block time.

Application of spectral analysis to meteorological and soil solution chemistry data

One of the longest-running forest ecosystem studies in Europe was established at the Solling region in central Germany. It provides a unique database covering more than 25 years of meteorological, throughfall, and soil solution chemistry data. It determines input to the ecosystem at canopy level and infiltration into the soil. These are important parts of water and element flux. Application of time series analysis can detect structures and dynamics in the long-term time series, which are not apparent in the original data. Our study investigates these dynamics, especially their seasonal patterns using spectral analysis. While some meteorological input parameters such as temperature and rain chemistry show pronounced annual seasonality, throughfall data of spruce and beech indicate a transformation of the input signal. Chemical parameters of spruce throughfall behave either half-annual seasonal (NO,-N, S04-S, Ca), or, as chloride, the time series contains both annual and half-annual seasonality. No element of beech throughfall has distinct seasonal behaviour. During infiltration into soil, the next step of flux, most seasonality disappears. Influences of seasonal inputs can be observed down to Ocm (under humus layer) or at most 10cm under beech forest. Only potassium under spruce forest still shows strong seasonality at 100cm soil depth. This appears to be coupled with a biological process.

The causality between budget deficit and interest rates in Japan: an application of time series analysis

Applying the Engle-Granger two-step procedure, this study finds that neither budget deficits, short-term interest rates and prices in one model nor budget deficits and longterm interest rates in the other model are cointegrated in Japan. Moreover, this study finds no causality between budget deficits and long-term interest rates but detects feedback causality between budget deficits and short-term interest rates in Japan using Hsiao's version of the Granger causality method with the aid of cointegration.