A hierarchical spatiotemporal analog forecasting model for count data

McDermott, Patrick L.; Wikle, Christopher K.; Millspaugh, Joshua J.

doi:10.1002/ece3.3621

Cited by 5 publications

(10 citation statements)

References 45 publications

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“…In this section, we describe how the similarities between climate forcings at different times can be used to model the temporal dependence in the offset term. Following McDermott and Wikle (2016) and McDermott et al (2018), who take a similar approach to forecasting soil moisture and waterfowl settling behavior, we will refer to this as an analogue prior on γ t , t = 1, . .…”

Section: Analogue Priormentioning

confidence: 99%

“…Analogue methods were originally developed as empirical tools for short-term weather forecasting (Krick, 1942) and climate modeling (Barnett and Preisendorfer, 1978), but researchers have begun to recognize their utility in a variety of contexts, including machine learning (Zhao and Giannakis, 2016;Lguensat et al, 2017), wind-speed modeling (Nagarajan et al, 2015), and air quality monitoring (Delle Monache et al, 2014). Historically, analogue methods have been empirical, somewhat ad-hoc tools, but recently there have been attempts at putting these into a probabilistic framework (McDermott and Wikle, 2016;McDermott et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Projecting Flood-Inducing Precipitation with a Bayesian Analogue Model

Bopp

Shaby

Forest

et al. 2020

JABES

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The hazard of pluvial flooding is largely influenced by the spatial and temporal dependence characteristics of precipitation. When extreme precipitation possesses strong spatial dependence, the risk of flooding is amplified due to catchment factors that cause runoff accumulation such as topography. Temporal dependence can also increase flood risk as storm water drainage systems operating at capacity can be overwhelmed by heavy precipitation occurring over multiple days. While transformed Gaussian processes are common choices for modeling precipitation, their weak tail dependence may lead to underestimation of flood risk. Extreme value models such as the generalized Pareto processes for threshold exceedances and max-stable models are attractive alternatives, but are difficult to fit when the number of observation sites is large, and are of little use for modeling the bulk of the distribution, which may also be of interest to water management planners. While the atmospheric dynamics governing precipitation are complex and difficult to fully incorporate into a parsimonious statistical model, non-mechanistic analogue methods that approximate those dynamics have proven to be promising approaches to capturing the temporal dependence of precipitation. In this paper, we present a Bayesian analogue method that leverages large, synoptic-scale atmospheric patterns to make precipitation forecasts. Changing spatial dependence across varying intensities is modeled as a mixture of spatial Student-t processes that can accommodate both strong and weak tail dependence. The proposed model demonstrates improved performance at capturing the distribution of extreme precipitation over Community Atmosphere Model (CAM) 5.2 forecasts.

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Section: Analogue Priormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Projecting Flood-Inducing Precipitation with a Bayesian Analogue Model

Bopp

Shaby

Forest

et al. 2020

JABES

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“…Forecasting is widely used for a wide range of scholarship and purposes, such as for data calculations [7], weather forecasting [8] and also used in foreign exchange [9].…”

Section: Forecastingmentioning

confidence: 99%

Supply Chain Management Implementation on Snacks Production Process

Choeriyah

Setiawan

2019

International Journal of New Media Technology

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PQR's company is a company that located in Citeureup Tasikmalaya. The company is engaged in the production of snacks such as "mie lidi" or lidi noodles. It was found some problems that happened in this company such as purchasing department have a problem to determine the number of raw material requirements to the supplier. It happened because of the uncertain demands from the distributors. Besides a problem in the upstream, there was some problem in the downstream that was shipping department was difficult to make a schedule of product shipping. Based on the problems, so the company needs the development of the system with by supply chain management approach. A strategy that used in the production process is made to stock. The method that used is forecasting method single moving average based on sales data. The inventory calculation of the safe limits of raw materials and products use safety stock method. After testing has done, then it gets the conclusion from the result that this information system is making easy purchasing department to determine the number of raw materials for procurement process to a supplier and it is making easy shipping department to make a schedule of product shipping for distributors.Index Terms-supply chain management, make-tostock, single moving average, safety stock I.

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“…In the mallard settling pattern application, the temporal embedding lag of the input process is m = one month and we used q = 50 lagged months. Both values were chosen based on the priors used by McDermott et al (2018).…”

Section: D-eesn With Glmmentioning

confidence: 99%

“…The count data within transect is reported per 29km segment. In this application, we considered BPS count data for mallards (Anas platyrhynchos) from 1925 segments with a spatial extent of 85 − 165°W and 43 − 69°N for the years 1970 to 2014 obtained from (https://migbirdapps.fws.gov) to compare to previous works (Wu et al, 2013;McDermott et al, 2018).…”

Section: Background and Datamentioning

confidence: 99%

Alternative learning strategies for spatio-temporal processes of complex animal behavior

Schafer¹

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The estimation of spatio-temporal dynamics of animal behavior processes is complicated by nonlinear interactions. Alternative learning methods such as machine learning, deep learning, and reinforcement learning have proven successful for approximating nonlinear system mechanisms for prediction and classification. These alternative learning frameworks can be linked to statistical models in a hierarchical framework to improve ecological inference and prediction in the presence of uncertainty. This dissertation provides three methodological extensions of alternative learning with statistical uncertainty quantification for modeling animal behavior dynamics at different scales. First, an efficient Bayesian Markov model is developed to provide inference on white-fronted geese behavior from individual accelerometer and location data while accounting for classification uncertainty. Second, nonlinear basis function expansions produced by a spatio-temporal echo state network are used as features in a hierarchical generalized linear model for predicting spatial patterns of mallard duck settling pattern counts. Lastly, Bayesian inverse reinforcement learning is developed to estimate the behavioral state costs for collective animal groups.

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A hierarchical spatiotemporal analog forecasting model for count data

Cited by 5 publications

References 45 publications

Projecting Flood-Inducing Precipitation with a Bayesian Analogue Model

Projecting Flood-Inducing Precipitation with a Bayesian Analogue Model

Supply Chain Management Implementation on Snacks Production Process

Alternative learning strategies for spatio-temporal processes of complex animal behavior

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