Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Comparison of access methods for time-evolving data
ACM Computing Surveys (CSUR)
Deformable Markov model templates for time-series pattern matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
On computing correlated aggregates over continual data streams
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Time series similarity measures and time series indexing (abstract only)
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Continuous queries over data streams
ACM SIGMOD Record
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Estimating the Selectivity of Spatial Queries Using the `Correlation' Fractal Dimension
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Evaluating the intrinsic dimension of evolving data streams
Proceedings of the 2006 ACM symposium on Applied computing
A fast and effective method to find correlations among attributes in databases
Data Mining and Knowledge Discovery
Measuring evolving data streams' behavior through their intrinsic dimension
New Generation Computing
BGP-lens: patterns and anomalies in internet routing updates
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Manipulation as a security mechanism in sensor networks
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Rise and fall patterns of information diffusion: model and implications
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 22nd international conference on World Wide Web companion
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Forecasting has attracted a lot of research interest, with very successful methods for periodic time series. Here, we propose a fast, automated method to do non-linear forecasting, for both periodic as well as chaotic time series. We use the technique of delay coordinate embedding, which needs several parameters; our contribution is the automated way of setting these parameters, using the concept of `intrinsic dimensionality'. Our operational system has fast and scalable algorithms for preprocessing and, using R-trees, also has fast methods for forecasting. The result of this work is a black-box which, given a time series as input, finds the best parameter settings, and generates a prediction system. Tests on real and synthetic data show that our system achieves low error, while it can handle arbitrarily large datasets.