Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Projections for efficient document clustering
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Nearest-Neighbours for Time Series
Applied Intelligence
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Stationary and Integrated Autoregressive Neural Network Processes
Neural Computation
A System for the Analysis of Jet Engine Vibration Data
Integrated Computer-Aided Engineering
Environmental Modelling & Software
Predictive modeling for wastewater applications: Linear and nonlinear approaches
Environmental Modelling & Software
Kalman filters and adaptive windows for learning in data streams
DS'06 Proceedings of the 9th international conference on Discovery Science
Components of an environmental observatory information system
Computers & Geosciences
Data-driven modeling approaches to support wastewater treatment plant operation
Environmental Modelling & Software
Automated Bayesian quality control of streaming rain gauge data
Environmental Modelling & Software
A concept of web-based energy data quality assurance and control system
Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services
Review: Data-derived soft-sensors for biological wastewater treatment plants: An overview
Environmental Modelling & Software
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The deployment of environmental sensors has generated an interest in real-time applications of the data they collect. This research develops a real-time anomaly detection method for environmental data streams that can be used to identify data that deviate from historical patterns. The method is based on an autoregressive data-driven model of the data stream and its corresponding prediction interval. It performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no pre-classification of anomalies. Furthermore, this method can be easily deployed on a large heterogeneous sensor network. Sixteen instantiations of this method are compared based on their ability to identify measurement errors in a windspeed data stream from Corpus Christi, Texas. The results indicate that a multilayer perceptron model of the data stream, coupled with replacement of anomalous data points, performs well at identifying erroneous data in this data stream.