SAX-quantile based multiresolution approach for finding heatwave events in summer temperature time series

Time series pattern discovery is of great importance in a large variety of environmental and engineering applications, from supporting predictive models to helping to understand hidden underlying processes. This work develops a multiresolution time series method for extracting patterns in weather records, particular temperature data. The topic is important, as, given a warming climate, morbidity and mortality are expected to rise as heatwave frequency and intensity increase. By analysing summer temperature quantiles at different levels of coarseness, it was found that compounding models can contain a complete description of severe weather events. This new multiresolution quantile approach is developed as an extension of the symbolic aggregate approximation of the temperature time series in which quantiles are computed at every stretch of the piecewise partition. The process is iterated at different scales of the partition, and it was found to be a very useful approach for finding patterns related to both heatwave periods and intensities. The method is successfully tested using real weather records from Brazil (Recife) and the UK (London), and it was found that in both locations heatwave intensity and frequency are increasing at a substantial rate. In addition, it was found that the rate of increase in intensity of the heatwaves is far outstripping the rate of increase in mean summer temperature: by a factor of 2 in Recife and a factor of 6 in London. The approach will be of use to those looking at the impact of future climates on civil engineering, water resources, energy use, agriculture and health care, or those looking for sustained extreme events in any time series.

[1]  Abdullah Mueen,et al.  Enumeration of time series motifs of all lengths , 2013, 2013 IEEE 13th International Conference on Data Mining.

[2]  Yuan Li,et al.  Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation , 2009, SSDBM.

[3]  B. Dousset,et al.  The Impact of Heat Islands on Mortality in Paris during the August 2003 Heat Wave , 2011, Environmental health perspectives.

[4]  Mohammed Al-Shalalfa,et al.  Efficient Periodicity Mining in Time Series Databases Using Suffix Trees , 2011, IEEE Transactions on Knowledge and Data Engineering.

[5]  Jeffrey D. Scargle,et al.  Wavelet and Other Multi-resolution Methods for Time Series Analysis , 1997 .

[6]  Romain Tavenard,et al.  1d-SAX: A Novel Symbolic Representation for Time Series , 2013, IDA.

[7]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[8]  Francisco Martínez-Álvarez.  Clustering preprocessing to improve time series forecasting , 2011, AI Commun..

[9]  Jeffrey D. Ullman,et al.  Mining of Massive Datasets: Data Mining , 2011 .

[10]  Qiang Wang,et al.  A multiresolution symbolic representation of time series , 2005, 21st International Conference on Data Engineering (ICDE'05).

[11]  Joaquín Izquierdo,et al.  Hybrid regression model for near real-time urban water demand forecasting , 2017, J. Comput. Appl. Math..

[12]  Ying Wah Teh,et al.  Clustering of large time series datasets , 2014, Intell. Data Anal..

[13]  Eamonn J. Keogh,et al.  Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases , 2001, Knowledge and Information Systems.

[14]  David Coley,et al.  A comparison of structural and behavioural adaptations to future proofing buildings against higher temperatures , 2012 .

[15]  C. Cassisi,et al.  Probabilistic Reasoning Over Seismic Time Series: Volcano Monitoring by Hidden Markov Models at Mt. Etna , 2016, Pure and Applied Geophysics.

[16]  Paulo J. Azevedo,et al.  Multiresolution Motif Discovery in Time Series , 2010, SDM.

[17]  David Coley,et al.  Comparison of multi-year and reference year building simulations , 2010 .

[18]  Li Wei,et al.  Experiencing SAX: a novel symbolic representation of time series , 2007, Data Mining and Knowledge Discovery.

[19]  Jean-Philippe Vidal,et al.  Using UKCP09 probabilistic climate information for UK water resource planning , 2012 .

[20]  José A. Romagnoli,et al.  Process data de-noising using wavelet transform , 1999, Intell. Data Anal..

[21]  Piotr Wojdyllo.  Wavelets and Mallat's Multiresolution Analysis , 1998, Fundam. Informaticae.

[22]  Eamonn J. Keogh,et al.  Experimental comparison of representation methods and distance measures for time series data , 2010, Data Mining and Knowledge Discovery.

[23]  David Coley,et al.  A comparison of future weather created from morphed observed weather and created by a weather generator , 2012 .

[24]  Taha B. M. J. Ouarda,et al.  An EMD and PCA hybrid approach for separating noise from signal, and signal in climate change detection , 2012 .

[25]  Dimitrios Gunopulos,et al.  Multiresolution Clustering of Time Series and Application to Images , 2007 .

[26]  Manuel Herrera,et al.  Quantile regression ensemble for summer temperatures time series and its impact on built environment studies , 2016 .

[27]  Milde M. S. Lira,et al.  Models Based on Neural Networks and Neuro-Fuzzy Systems for Wind Power Prediction Using Wavelet Transform as Data Preprocessing Method , 2012, EANN.

[28]  Durga Toshniwal,et al.  Feature extraction from time series data , 2009, J. Comput. Methods Sci. Eng..

[29]  Qiang Wang,et al.  Time series analysis with multiple resolutions , 2010, Inf. Syst..

[30]  A. Nebot,et al.  Multi-resolution time-series prediction using fuzzy inductive reasoning , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[31]  Ada Wai-Chee Fu,et al.  Efficient time series matching by wavelets , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[32]  Mathilde Pascal,et al.  How to estimate exposure when studying the temperature-mortality relationship? A case study of the Paris area , 2015, International Journal of Biometeorology.

[33]  Walter Vergara,et al.  Climate Impacts on Energy Systems: Key Issues for Energy Sector Adaptation , 2011 .

[34]  Eamonn J. Keogh.  Indexing and Mining Time Series Data , 2008, Encyclopedia of GIS.

[35]  Hee-Seok Oh,et al.  Multi-resolution time series analysis applied to solar irradiance and climate reconstructions , 2003 .

[36]  S J Ormerod,et al.  Evidence needed to manage freshwater ecosystems in a changing climate: turning adaptation principles into practice. , 2010, The Science of the total environment.

[37]  Zhihai Wang,et al.  A lazy associative classifier for time series , 2015, Intell. Data Anal..