Use of SARIMA models to assess data-poor fisheries: a case study with a sciaenid fishery off Portugal

Research on assessment and monitoring methods has primarily focused on fisheries with long multivariate data sets. Less research exists on methods applicable to data-poor fisheries with univariate data sets with a small sample size. In this study, we examine the capabilities of seasonal autoregressive integrated moving average (SARIMA) models to fit, forecast, and monitor the landings of such data-poor fisheries. We use a European fishery on meagre (Sciaenidae: Argyrosomus regius), where only a short time series of landings was available to model (n=60 months), as our case-study. We show that despite the limited sample size, a SARIMA model could be found that adequately fitted and forecasted the time series of meagre landings (12-month forecasts; mean error: 3.5 tons (t); annual absolute percentage error: 15.4%). We derive model-based prediction intervals and show how they can be used to detect problematic situations in the fishery. Our results indicate that over the course of one year the meagre landings remained within the prediction limits of the model and therefore indicated no need for urgent management intervention. We discuss the information that SARIMA model structure conveys on the meagre lifecycle and fishery, the methodological requirements of SARIMA forecasting of data-poor fisheries landings, and the capabilities SARIMA models present within current efforts to monitor the world’s data-poorest resources.

Removing observational noise from fisheries-independent time series data using ARIMA models

Abundance indices derived from fishery-independent surveys typically exhibit much higher interannual variability than is consistent with the within-survey variance or the life history of a species. This extra variability is essentially observation noise (i.e. measurement error); it probably reflects environmentally driven factors that affect catchability over time. Unfortunately, high observation noise reduces the ability to detect important changes in the underlying population abundance. In our study, a noise-reduction technique for uncorrelated observation noise that is based on autoregressive integrated moving average (ARIMA) time series modeling is investigated. The approach is applied to 18 time series of finfish abundance, which were derived from trawl survey data from the U.S. northeast continental shelf. Although the a priori assumption of a random-walk-plus-uncorrelated-noise model generally yielded a smoothed result that is pleasing to the eye, we recommend that the most appropriate ARIMA model be identified for the observed time series if the smoothed time series will be used for further analysis of the population dynamics of a species.

Climatic factors associated with phytoplankton community succession during and after the 1977 drought for the San Francisco Bay-Delta Estuary [abstract]

Principal coordinates analysis and multiple regression analysis were used to determine the environmental factors associated with the decline in phytoplankton production during and after the 1977 drought for the San Francisco Bay-Delta Estuary. Physical, chemical and biological data were collected semimonthly or monthly during the spring-summer between 1973 and 1982 from 15 sampling sites located throughout the Bay-Delta. A decline in phytoplankton community diversity and density during the 1977 drought and subsequent years (1978 through 1981) was described using principal coordinates analysis. The best multiple regression which described the changes in phytoplankton community succession contained the variables water temperature, wind velocity and ortho-phosphate concentration. Together these variables accounted for 61 percent of the variation in the phytoplankton community among years described by principal coordinates analysis. An increase in water temperature, wind velocity and ortho-phosphate concentration within the Bay-Delta, beginning in June 1976 and continuing through 1981, was demonstrated using weighted moving averages. From the strong association between phytoplankton community succession and climatic variables it was hypothesized that the decline in phytoplankton production during and after the 1977 drought was associated with climatic changes within the northeast Pacific.

Trend and growth analysis of marine fish production in Bangladesh

The study based on time series marine fish production data during the period of 1983-1984 to 2007-2008 in Bangladesh. For this growth analysis six deterministic time series models are considered. The estimated best fitting models are the cubic, quadratic and quadratic model is appropriate for industrial marine fish production, artisanal marine fish production and total marine fish production in Bangladesh respectively. The study attempts to provide forecasts of marine fish production in Bangladesh for the year of 2008-09 to 2012-13. The magnitude of instability in marine fish production was attempted by computing the coefficient of variation (CV) and the percentage deviation from three years moving average values. The study revealed that the total marine fish production was observed to be relatively stable (CV being 31.85%) compared to the artisanal marine fish production (CV being 32.04%) and industrial marine fish (CV being 47.20%). For the three components of marine fish production the growth rates were different over different time points. The variation of the growth rates in industrial marine fish production was -21.6% to 13.12%, in artisanal marine fish production was 2.39% to 5.29% and in total marine fish production was 11.23% to 24.85% during the study period.

Segmenting Foreground Objects from a Dynamic Textured Background via a Robust Kalman Filter

The algorithm presented in this paper aims to segment the foreground objects in video (e.g., people) given time-varying, textured backgrounds. Examples of time-varying backgrounds include waves on water, clouds moving, trees waving in the wind, automobile traffic, moving crowds, escalators, etc. We have developed a novel foreground-background segmentation algorithm that explicitly accounts for the non-stationary nature and clutter-like appearance of many dynamic textures. The dynamic texture is modeled by an Autoregressive Moving Average Model (ARMA). A robust Kalman filter algorithm iteratively estimates the intrinsic appearance of the dynamic texture, as well as the regions of the foreground objects. Preliminary experiments with this method have demonstrated promising results.

Quantifying the value of improved wind energy forecasts in a pool-based electricity market

This work illustrates the influence of wind forecast errors on system costs, wind curtailment and generator dispatch in a system with high wind penetration. Realistic wind forecasts of different specified accuracy levels are created using an auto-regressive moving average model and these are then used in the creation of day-ahead unit commitment schedules. The schedules are generated for a model of the 2020 Irish electricity system with 33% wind penetration using both stochastic and deterministic approaches. Improvements in wind forecast accuracy are demonstrated to deliver: (i) clear savings in total system costs for deterministic and, to a lesser extent, stochastic scheduling; (ii) a decrease in the level of wind curtailment, with close agreement between stochastic and deterministic scheduling; and (iii) a decrease in the dispatch of open cycle gas turbine generation, evident with deterministic, and to a lesser extent, with stochastic scheduling.

Temporal variations of IEEE 802.11b signal strengths in an in-building environment

Recent popularity of the IEEE 802.11b Wireless Local Area Networks (WLANs) in a host of current-day applications has instigated a suite of research challenges. The 802.11b WLANs are highly reliable and wide spread. In this work, we study the temporal characteristics of RSSI in the real-working environment by conducting a controlled set of experiments. Our results indicate that a significant variability in the RSSI can occur over time. Some of this variability in the RSSI may be due to systematic causes while the other component can be expressed as stochastic noise. We present an analysis of both these aspects of RSSI. We treat the moving average of the RSSI as the systematic causes and the noise as the stochastic causes. We give a reasonable estimate for the moving average to compute the noise accurately. We attribute the changes in the environment such as the movement of people and the noise associated with the NIC circuitry and the network access point as causes for this variability. We find that the results of our analysis are of primary importance to active research areas such as location determination of users in a WLAN. The techniques used in some of the RF-based WLAN location determination systems, exploit the characteristics of the RSSI presented in this work to infer the location of a wireless client in a WLAN. Thus our results form the building blocks for other users of the exact characteristics of the RSSI.

Quasiexperimental Study of the Effects of Antibiotic Use, Gastric Acid-Suppressive Agents, and Infection Control Practices on the Incidence of Clostridium difficile-Associated Diarrhea in Hospitalized Patients

The objective of this study was to evaluate the effects of antimicrobial drug use, gastric acid-suppressive agent use, and infection control practices on the incidence of Clostridium difficile-associated diarrhea (CDAD) in a 426-bed general teaching hospital in Northern Ireland. The study was retrospective and ecological in design. A multivariate autoregressive integrated moving average (time-series analysis) model was built to relate CDAD incidence with antibiotic use, gastric acid-suppressive agent use, and infection control practices within the hospital over a 5-year period (February 2002 to March 2007). The findings of this study showed that temporal variation in CDAD incidence followed temporal variations in expanded-spectrum cephalosporin use (average delay = 2 months; variation of CDAD incidence = 0.01/100 bed-days), broad-spectrum cephalosporin use (average delay = 2 months; variation of CDAD incidence = 0.02/100 bed-days), fluoroquinolone use (average delay = 3 months; variation of CDAD incidence = 0.004/100 bed-days), amoxicillin-clavulanic acid use (average delay = 1 month; variation of CDAD incidence = 0.002/100 bed-days), and macrolide use (average delay = 5 months; variation of CDAD incidence = 0.002/100 bed-days). Temporal relationships were also observed between CDAD incidence and use of histamine-2 receptor antagonists (H2RAs; average delay = 1 month; variation of CDAD incidence = 0.001/100 bed-days). The model explained 78% of the variance in the monthly incidence of CDAD. The findings of this study highlight a temporal relationship between certain classes of antibiotics, H2RAs, and CDAD incidence. The results of this research can help hospitals to set priorities for restricting the use of specific antibiotic classes, based on the size-effect of each class and the delay necessary to observe an effect.

The impact of antibiotic use on the incidence and resistance pattern of extended-spectrum beta-lactamase-producing bacteria in primary and secondary healthcare settings

Aims: The objective of the present study was to study the relationship between hospital antibiotic use, community antibiotic use and the incidence of extended-spectrum beta-lactamase (ESBL)-producing bacteria in hospitals, while assessing the impact of a fluoroquinolone restriction policy on ESBL-producing bacteria incidence rates. METHODS: The study was retrospective and ecological in design. A multivariate autoregressive integrated moving average (ARIMA) model was built to relate antibiotic use to ESB-producing bacteria incidence rates and resistance patterns over a 5 year period (January 2005-December 2009). Results: Analysis showed that the hospital incidence of ESBLs had a positive relationship with the use of fluoroquinolones in the hospital (coefficient = 0.174, P= 0.02), amoxicillin-clavulanic acid in the community (coefficient = 1.03, P= 0.03) and mean co-morbidity scores for hospitalized patients (coefficient = 2.15, P= 0.03) with various time lags. The fluoroquinolone restriction policy was implemented successfully with the mean use of fluoroquinolones (mainly ciprofloxacin) being reduced from 133 to 17 defined daily doses (DDDs)/1000 bed days (P <0.001) and from 0.65 to 0.54 DDDs/1000 inhabitants/day (P= 0.0007), in both the hospital and its surrounding community, respectively. This was associated with an improved ciprofloxacin susceptibility in both settings [ciprofloxacin susceptibility being improved from 16% to 28% in the community (P <0.001)] and with a statistically significant reduction in ESBL-producing bacteria incidence rates. Discussion: This study supports the value of restricting the use of certain antimicrobial classes to control ESBL, and demonstrates the feasibility of reversing resistance patterns post successful antibiotic restriction. The study also highlights the potential value of the time-series analysis in designing efficient antibiotic stewardship. © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

Hospital antibiotic use and its relationship to age-adjusted comorbidity and alcohol-based hand rub consumption

SUMMARY The objective of this study was to evaluate the effect of age-adjusted comorbidity and alcohol-based hand rub on monthly hospital antibiotic usage, retrospectively. A multivariate autoregressive integrated moving average (ARIMA) model was built to relate the monthly use of all antibiotics grouped together with age-adjusted comorbidity and alcohol-based hand rub over a 5-year period (April 2005-March 2010). The results showed that monthly antibiotic use was positively related to the age-adjusted comorbidity index (concomitant effect, coefficient 1·103, P = 0·0002), and negatively related to the use of alcohol-based hand rub (2-month delay, coefficient -0·069, P = 0·0533). Alcohol-based hand rub is considered a modifiable factor and as such can be identified as a target for quality improvement programmes. Time-series analysis may provide a suitable methodology for identifying possible predictive variables that explain antibiotic use in healthcare settings. Future research should examine the relationship between infection control practices and antibiotic use, identify other infection control predictive factors for hospital antibiotic use, and evaluate the impact of enhancing different infection control practices on antibiotic use in a healthcare setting.

Numerical techniques for modeling Doppler ultrasound spectra systems

Evaluation of blood-flow Doppler ultrasound spectral content is currently performed on clinical diagnosis. Since mean frequency and bandwidth spectral parameters are determinants on the quantification of stenotic degree, more precise estimators than the conventional Fourier transform should be seek. This paper summarizes studies led by the author in this field, as well as the strategies used to implement the methods in real-time. Regarding stationary and nonstationary characteristics of the blood-flow signal, different models were assessed. When autoregressive and autoregressive moving average models were compared with the traditional Fourier based methods in terms of their statistical performance while estimating both spectral parameters, the Modified Covariance model was identified by the cost/benefit criterion as the estimator presenting better performance. The performance of three time-frequency distributions and the Short Time Fourier Transform was also compared. The Choi-Williams distribution proved to be more accurate than the other methods. The identified spectral estimators were developed and optimized using high performance techniques. Homogeneous and heterogeneous architectures supporting multiple instruction multiple data parallel processing were essayed. Results obtained proved that real-time implementation of the blood-flow estimators is feasible, enhancing the usage of more complex spectral models on other ultrasonic systems.

Asymptotic Distribution of a Simple Linear Estimator for VARMA Models in Echelon Form

In this paper, we study the asymptotic distribution of a simple two-stage (Hannan-Rissanen-type) linear estimator for stationary invertible vector autoregressive moving average (VARMA) models in the echelon form representation. General conditions for consistency and asymptotic normality are given. A consistent estimator of the asymptotic covariance matrix of the estimator is also provided, so that tests and confidence intervals can easily be constructed.

Mortalité par suicide au Canada depuis le début du XXe siècle : perspectives sociodémographiques et macroéconomiques

Robert Bourbeau, département de démographie (Directeur de recherche) Marianne Kempeneers, département de sociologie (Codirectrice de recherche)

ARMA modeling of time series based on rational approximation of spectral density function

This study is concerned with Autoregressive Moving Average (ARMA) models of time series. ARMA models form a subclass of the class of general linear models which represents stationary time series, a phenomenon encountered most often in practice by engineers, scientists and economists. It is always desirable to employ models which use parameters parsimoniously. Parsimony will be achieved by ARMA models because it has only finite number of parameters. Even though the discussion is primarily concerned with stationary time series, later we will take up the case of homogeneous non stationary time series which can be transformed to stationary time series. Time series models, obtained with the help of the present and past data is used for forecasting future values. Physical science as well as social science take benefits of forecasting models. The role of forecasting cuts across all fields of management-—finance, marketing, production, business economics, as also in signal process, communication engineering, chemical processes, electronics etc. This high applicability of time series is the motivation to this study.

Desarrollo de un nuevo protocolo de cirugía general, que disminuya las devoluciones de medicamentos e insumos al almacén de cirugía del Hospital Méderi

Esta tesis surge como una oportunidad de mejora en el almacén de cirugías del Hospital MÉDERI, debido a la recurrente devolución de medicamentos e insumos solicitados por las auxiliares de enfermería para las cirugías generales, lo cual repercute directamente en pérdidas de productividad laboral por los re-procesos, un aumento en los errores humanos y posibles pérdidas de medicamentos e insumos. Tras esta clara oportunidad de mejora, se toma la decisión de evaluar el proceso interno del almacén de cirugías con el fin de conocer el punto crítico que genera esta situación; dando como resultado los protocolos de cirugías, los cuales al haber sido diseñados varios años atrás basados en una demanda presentada en ese momento, no están acorde con la realidad que se vive actualmente el almacén de cirugía. Por lo tanto se decidió implementar un pronóstico de promedio móvil, para identificar la demanda real que se presentan en el Hospital MÉDERI, esto seguido de una identificación gráfica comparativa que permitiera definir el nuevo protocolo de cirugía general, lo cual permite disminuir la cantidad de material solicitado, con lo cual se generan disminuciones significativas en el inventario, perdidas y un aumento en la productividad.