29,859 research outputs found
Hierarchy-guided Model Selection for Time Series Forecasting
Generalizability of time series forecasting models depends on the quality of
model selection. Temporal cross validation (TCV) is a standard technique to
perform model selection in forecasting tasks. TCV sequentially partitions the
training time series into train and validation windows, and performs
hyperparameter optmization (HPO) of the forecast model to select the model with
the best validation performance. Model selection with TCV often leads to poor
test performance when the test data distribution differs from that of the
validation data. We propose a novel model selection method, H-Pro that exploits
the data hierarchy often associated with a time series dataset. Generally, the
aggregated data at the higher levels of the hierarchy show better
predictability and more consistency compared to the bottom-level data which is
more sparse and (sometimes) intermittent. H-Pro performs the HPO of the
lowest-level student model based on the test proxy forecasts obtained from a
set of teacher models at higher levels in the hierarchy. The consistency of the
teachers' proxy forecasts help select better student models at the
lowest-level. We perform extensive empirical studies on multiple datasets to
validate the efficacy of the proposed method. H-Pro along with off-the-shelf
forecasting models outperform existing state-of-the-art forecasting methods
including the winning models of the M5 point-forecasting competition
Priorities in the Location of Multiple Public Facilities
A collective decision problem is described by a set of agents, a profile of single-peaked preferences over the real line and a number k of public facilities to be located. We consider public facilities that do not suffer from congestion and are non-excludable. We provide a characterization of the class of rules satisfying Pareto-efficiency, object-population monotonicity and sovereignty. Each rule in the class is a priority rule that selects locations according to a predetermined priority ordering among "interest groups". We characterize each of the subclasses of priority rules that respectively satisfy anonymity, hiding-proofness and strategy-proofness. In particular, we prove that a priority rule is strategy-proof if and only if it partitions the set of agents into a fixed hierarchy. Alternatively, any such rule can be viewed as a collection of fixed-populations generalized peak-selection median rules (Moulin, 1980), that are linked across populations, in a way that we describe.Multiple public facilities; Priority rules; Hierarchical rules; Object-population monotonicity; Sovereignty; Anonymity; Strategy-proofness; Generalized median rules; Hiding-proofness
On morphological hierarchical representations for image processing and spatial data clustering
Hierarchical data representations in the context of classi cation and data
clustering were put forward during the fties. Recently, hierarchical image
representations have gained renewed interest for segmentation purposes. In this
paper, we briefly survey fundamental results on hierarchical clustering and
then detail recent paradigms developed for the hierarchical representation of
images in the framework of mathematical morphology: constrained connectivity
and ultrametric watersheds. Constrained connectivity can be viewed as a way to
constrain an initial hierarchy in such a way that a set of desired constraints
are satis ed. The framework of ultrametric watersheds provides a generic scheme
for computing any hierarchical connected clustering, in particular when such a
hierarchy is constrained. The suitability of this framework for solving
practical problems is illustrated with applications in remote sensing
QuickCast: Fast and Efficient Inter-Datacenter Transfers using Forwarding Tree Cohorts
Large inter-datacenter transfers are crucial for cloud service efficiency and
are increasingly used by organizations that have dedicated wide area networks
between datacenters. A recent work uses multicast forwarding trees to reduce
the bandwidth needs and improve completion times of point-to-multipoint
transfers. Using a single forwarding tree per transfer, however, leads to poor
performance because the slowest receiver dictates the completion time for all
receivers. Using multiple forwarding trees per transfer alleviates this
concern--the average receiver could finish early; however, if done naively,
bandwidth usage would also increase and it is apriori unclear how best to
partition receivers, how to construct the multiple trees and how to determine
the rate and schedule of flows on these trees. This paper presents QuickCast, a
first solution to these problems. Using simulations on real-world network
topologies, we see that QuickCast can speed up the average receiver's
completion time by as much as while only using more
bandwidth; further, the completion time for all receivers also improves by as
much as faster at high loads.Comment: [Extended Version] Accepted for presentation in IEEE INFOCOM 2018,
Honolulu, H
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