6,679,634 research outputs found
Generalizing the first-difference correlated random walk for marine animal movement data
Animal telemetry data are often analysed with discrete time movement models
assuming rotation in the movement. These models are defined with equidistant
distant time steps. However, telemetry data from marine animals are observed
irregularly. To account for irregular data, a time-irregularised
first-difference correlated random walk model with drift is introduced. The
model generalizes the commonly used first-difference correlated random walk
with regular time steps by allowing irregular time steps, including a drift
term, and by allowing different autocorrelation in the two coordinates. The
model is applied to data from a ringed seal collected through the Argos
satellite system, and is compared to related movement models through
simulations. Accounting for irregular data in the movement model results in
accurate parameter estimates and reconstruction of movement paths. Measured by
distance, the introduced model can provide more accurate movement paths than
the regular time counterpart. Extracting accurate movement paths from uncertain
telemetry data is important for evaluating space use patterns for marine
animals, which in turn is crucial for management. Further, handling irregular
data directly in the movement model allows efficient simultaneous analysis of
several animals
Mining data streams using option trees (revised edition, 2004)
The data stream model for data mining places harsh restrictions on a learning algorithm. A model must be induced following the briefest interrogation of the data, must use only available memory and must update itself over time within these constraints. Additionally, the model must be able to be used for data mining at any point in time.
This paper describes a data stream classi_cation algorithm using an ensemble of option trees. The ensemble of trees is induced by boosting and iteratively combined into a single interpretable model. The algorithm is evaluated using benchmark datasets for accuracy against state-of-the-art algorithms that make use of the entire dataset
A Bayesian Nonparametric Markovian Model for Nonstationary Time Series
Stationary time series models built from parametric distributions are, in
general, limited in scope due to the assumptions imposed on the residual
distribution and autoregression relationship. We present a modeling approach
for univariate time series data, which makes no assumptions of stationarity,
and can accommodate complex dynamics and capture nonstandard distributions. The
model for the transition density arises from the conditional distribution
implied by a Bayesian nonparametric mixture of bivariate normals. This implies
a flexible autoregressive form for the conditional transition density, defining
a time-homogeneous, nonstationary, Markovian model for real-valued data indexed
in discrete-time. To obtain a more computationally tractable algorithm for
posterior inference, we utilize a square-root-free Cholesky decomposition of
the mixture kernel covariance matrix. Results from simulated data suggest the
model is able to recover challenging transition and predictive densities. We
also illustrate the model on time intervals between eruptions of the Old
Faithful geyser. Extensions to accommodate higher order structure and to
develop a state-space model are also discussed
Estimating the Output Gap Using Business Survey Data - A Bivariate Structural Time Series Model for the German Economy
This paper deals with the estimation of the output gap. We use uni- and bivariate unobserved components models in order to decompose the observed German GDP-series into trend, cycle and seasonal components. The results show that using the ifo business assessment variable as an indicator for the cycle the estimation of the output gap is much more precise and out-of-sample forecasts exhibit smaller prediction errors.Output gap, unobserved component models, survey data
A model of rainfall based on finite-state cellular automata
The purpose of this paper is to demonstrate that a finite state cellular
automata model is suitable for modeling rainfall in the space-time plane. The
time-series properties of the simulated series are matched with historical rainfall
data gathered from Whenuapai, NZ. The spatial scale of the model cells
in related to land-area by optimizing the cross-correlation between sites at lag
0 relative to rainfall data collected from Auckland, NZ. The model is shown
to be adequate for simulation in time, but inadequate in spatial dimension for
short distances
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