Locally adaptive dimensionality reduction for indexing large time series databases
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
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Finding similarity in time series data by method of time weighted moments
ADC '05 Proceedings of the 16th Australasian database conference - Volume 39
Classification of household devices by electricity usage profiles
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Hi-index | 0.00 |
Many scientific and business domains require the collection and analysis of time series data. Feature extraction is an important component of time series data mining. In this paper, we introduce simple and novel techniques for feature extraction from time series data based on moments and slopes. The proposed techniques are capable of handling vertical and horizontal shifts existing between time sequences. They can also handle global scaling and shrinking of the time sequences.