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» A new evolutionary method for time series forecasting
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ICPR
2010
IEEE
13 years 10 months ago
Temporal Extension of Laplacian Eigenmaps for Unsupervised Dimensionality Reduction of Time Series
—A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently time series data. In this embedded-based approach,...
Michal Lewandowski, Jesus Martinez-Del-Rincon, Dim...
VLDB
2007
ACM
121views Database» more  VLDB 2007»
14 years 8 months ago
Ranked Subsequence Matching in Time-Series Databases
Existing work on similar sequence matching has focused on either whole matching or range subsequence matching. In this paper, we present novel methods for ranked subsequence match...
Wook-Shin Han, Jinsoo Lee, Yang-Sae Moon, Haifeng ...
SIGMETRICS
2012
ACM
248views Hardware» more  SIGMETRICS 2012»
11 years 10 months ago
Pricing cloud bandwidth reservations under demand uncertainty
In a public cloud, bandwidth is traditionally priced in a pay-asyou-go model. Reflecting the recent trend of augmenting cloud computing with bandwidth guarantees, we consider a n...
Di Niu, Chen Feng, Baochun Li
ISNN
2011
Springer
12 years 10 months ago
Orthogonal Feature Learning for Time Series Clustering
This paper presents a new method that uses orthogonalized features for time series clustering and classification. To cluster or classify time series data, either original data or...
Xiaozhe Wang, Leo Lopes
PPSN
2010
Springer
13 years 6 months ago
General Lower Bounds for the Running Time of Evolutionary Algorithms
Abstract. We present a new method for proving lower bounds in evolutionary computation based on fitness-level arguments and an additional condition on transition probabilities bet...
Dirk Sudholt