UH Biocomputation Group - Functional depthhttp://biocomputation.herts.ac.uk/2022-05-26T15:31:48+01:00On Projection Methods for Functional Time Series Forecasting2022-05-26T15:31:48+01:002022-05-26T15:31:48+01:00Yi Suntag:biocomputation.herts.ac.uk,2022-05-26:/2022/05/26/on-projection-methods-for-functional-time-series-forecasting.html<p class="first last">Yi Sun's Journal Club session where she will talk about a paper "On Projection Methods for Functional Time Series Forecasting"</p>
<p>This week on Journal Club session Yi Sun will talk about a paper "On Projection Methods for Functional Time Series Forecasting".</p>
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<p>Two nonparametric methods are presented for forecasting functional time series
(FTS). The FTS we observe is a curve at a discrete-time point. We address both
one-step-ahead forecasting and dynamic updating. Dynamic updating is a forward
prediction of the unobserved segment of the most recent curve. Among the two
proposed methods, the first one is a straightforward adaptation to FTS of the
k-nearest neighbors methods for univariate time series forecasting. The second
one is based on a selection of curves, termed the curve envelope, that aims to
be representative in shape and magnitude of the most recent functional
observation, either a whole curve or the observed part of a partially observed
curve. In a similar fashion to k-nearest neighbors and other projection methods
successfully used for time series forecasting, we "project" the k-nearest
neighbors and the curves in the envelope for forecasting. In doing so, we keep
track of the next period evolution of the curves. The methods are applied to
simulated data, daily electricity demand, and NOx emissions and provide
competitive results with and often superior to several benchmark predictions.
The approach offers a model-free alternative to statistical methods based on
FTS modeling to study the cyclic or seasonal behavior of many FTS.</p>
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<p>Papers:</p>
<ul class="simple">
<li>A. Elías, R. Jiménez, H. Shang, <a class="reference external" href="https://doi.org/10.1016/j.jmva.2021.104890">"On Projection Methods for Functional Time Series Forecasting"</a>, 2022, Journal of Multivariate Analysis, 189, 104890</li>
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<p><strong>Date:</strong> 2022/05/27 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>