On the bursty evolution of blogspace
WWW '03 Proceedings of the 12th international conference on World Wide Web
Time series forecasting: Obtaining long term trends with self-organizing maps
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Strengthening the Forward Variable Selection Stopping Criterion
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
OP-ELM: optimally pruned extreme learning machine
IEEE Transactions on Neural Networks
Gene selection in time-series gene expression data
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Analysis of fast input selection: application in time series prediction
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Model-based search in large time series databases
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Distance measure for querying sequences of temporal intervals
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Input selection for long-term prediction of time series
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Direct and recursive prediction of time series using mutual information selection
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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This paper proposes a methodology using a fast variable selection as a modified version of the Forward-Backward algorithm. This methodology is adapted to the specificities of the data used: very small number of samples and high number of variables. Such data is generated using underlying dependencies and seasonality assumptions, from Meme phrases volume data. By the use of a resampling technique along with the proposed variable selection scheme, significant results are obtained, and the test Normalized Mean Square Error performances are improved. The results indicate that with the assumptions made on the data structure, variable selection is desirable. Also, the obtained information on the selected variables seem to cluster the time series in two very different classes: a set of approximately 600 series, which yield good NMSE, and seem to require very similar sets of variables for the prediction; and another set of 300--400 series, for which only the previous series value is of interest for the prediction. This first analysis clearly illustrates the future need to perform a more thorough analysis of the selected variables for each of the batch of series. Also, taking a close look at the possible dependences between the series inside a batch should give information as to why and how they are similar and have found themselves to be grouped under the same batch.