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The Analysis of Time Series: An Introduction
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In this paper, simple descriptive techniques for time series estimation in the time domain forecasting stationary processes in the frequency domain spectral analysis bivariate processes linear systems state-space models and the Kalman filter non-linear models multivariate time series modelling some other topics.Abstract:
Simple descriptive techniques probability models for time series estimation in the time domain forecasting stationary processes in the frequency domain spectral analysis bivariate processes linear systems state-space models and the Kalman filter non-linear models multivariate time series modelling some other topics.read more
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Revisiting the association between sea surface temperature and the epidemiology of fish poisoning in the South Pacific: reassessing the link between ciguatera and climate change.
TL;DR: A qualitative assessment of ciguatera intensity and sea surface temperature (SST) behaviour within the EEZ of selected South Pacific nations supported the notion that ciguatora intensity was highest when SST was between an upper and lower limit, and supports the possibility that when considering the impact of climate change on ciguaters, one has to consider two thresholds.
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Trend filtering via empirical mode decompositions
TL;DR: A nonparametric technique called empirical mode decomposition trend filtering is developed, based on an empirical analysis of the EMD, and an automatic procedure for selecting the requisite intrinsic mode functions is proposed.
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Development of New Hybrid Model of Discrete Wavelet Decomposition and Autoregressive Integrated Moving Average (ARIMA) Models in Application to One Month Forecast the Casualties Cases of COVID-19.
TL;DR: The proposed hybrid methodology involves the application of discreet wavelet decomposition to the dataset of deaths due to COVID-19, which splits the input data into component series and then applying an appropriate econometric model to each of the component series for making predictions of death cases in future.
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Improved methods to assess water yield changes from paired-catchment studies: application to the Maroondah catchments
TL;DR: In this article, a seasonal regression model with lag-one auto-regressive (AR1) error was developed to predict monthly streamflow at treated catchments based on streamflow data from a control catchment.
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Diurnal and tidal movements of snapper (Pagrus auratus, Sparidae) in an estuarine environment
TL;DR: It is demonstrated that in this system, snapper occupy relatively small and discrete areas of soft sediment seafloor, within which repeated, predictable movements are made.