| Matrix profile ii: Exploiting a novel algorithm and gpus to break the one hundred million barrier for time series motifs and joins Y Zhu, Z Zimmerman, NS Senobari, CCM Yeh, G Funning, A Mueen, ... 2016 IEEE 16th international conference on data mining (ICDM), 739-748, 2016 | 243 | 2016 |
| Time series joins, motifs, discords and shapelets: a unifying view that exploits the matrix profile CCM Yeh, Y Zhu, L Ulanova, N Begum, Y Ding, HA Dau, Z Zimmerman, ... Data Mining and Knowledge Discovery 32, 83-123, 2018 | 133 | 2018 |
| Matrix profile XI: SCRIMP++: time series motif discovery at interactive speeds Y Zhu, CCM Yeh, Z Zimmerman, K Kamgar, E Keogh 2018 IEEE International Conference on Data Mining (ICDM), 837-846, 2018 | 115 | 2018 |
| An open-source compiler and PCB synthesis tool for digital microfluidic biochips D Grissom, C Curtis, S Windh, C Phung, N Kumar, Z Zimmerman, ... INTEGRATION, the VLSI journal 51, 169-193, 2015 | 68 | 2015 |
| Matrix profile XIV: scaling time series motif discovery with GPUs to break a quintillion pairwise comparisons a day and beyond Z Zimmerman, K Kamgar, NS Senobari, B Crites, G Funning, P Brisk, ... Proceedings of the ACM Symposium on Cloud Computing, 74-86, 2019 | 66* | 2019 |
| Matrix profile xx: Finding and visualizing time series motifs of all lengths using the matrix profile F Madrid, S Imani, R Mercer, Z Zimmerman, N Shakibay, E Keogh 2019 IEEE International Conference on Big Knowledge (ICBK), 175-182, 2019 | 49 | 2019 |
| Super‐efficient cross‐correlation (SEC‐C): A fast matched filtering code suitable for desktop computers NS Senobari, GJ Funning, E Keogh, Y Zhu, CCM Yeh, Z Zimmerman, ... Seismological Research Letters 90 (1), 322-334, 2019 | 39 | 2019 |
| Exploiting a novel algorithm and GPUs to break the ten quadrillion pairwise comparisons barrier for time series motifs and joins Y Zhu, Z Zimmerman, N Shakibay Senobari, CCM Yeh, G Funning, ... Knowledge and Information Systems 54, 203-236, 2018 | 38 | 2018 |
| The Swiss army knife of time series data mining: ten useful things you can do with the matrix profile and ten lines of code Y Zhu, S Gharghabi, DF Silva, HA Dau, CCM Yeh, N Shakibay Senobari, ... Data Mining and Knowledge Discovery 34, 949-979, 2020 | 28 | 2020 |
| PCB escape routing and layer minimization for digital microfluidic biochips J McDaniel, Z Zimmerman, D Grissom, P Brisk IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2016 | 16 | 2016 |
| Matrix Profile XVIII: Time Series Mining in the Face of Fast Moving Streams using a Learned Approximate Matrix Profile Z Zimmerman, NS Senobari, G Funning, E Papalexakis, S Oymak, P Brisk, ... IEEE International Conference on Data Mining, 2019 | 15 | 2019 |
| Matrix profile xvii: Indexing the matrix profile to allow arbitrary range queries Y Zhu, CCM Yeh, Z Zimmerman, E Keogh 2020 IEEE 36th International Conference on Data Engineering (ICDE), 1846-1849, 2020 | 11 | 2020 |
| FA-LAMP: fpga-accelerated learned approximate matrix profile for time series similarity prediction A Kalantar, Z Zimmerman, P Brisk 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom …, 2021 | 8 | 2021 |
| Using the similarity matrix profile to investigate foreshock behavior of the 2004 parkfield earthquake N Shakibay Senobari, G Funning, Z Zimmerman, Y Zhu, E Keogh AGU Fall Meeting Abstracts 2018, S51B-03, 2018 | 7 | 2018 |
| Matrix Profile XXVI: Mplots: Scaling Time Series Similarity Matrices to Massive Data M Shahcheraghi, R Mercer, JMDA Rodrigues, A Der, HFS Gamboa, ... 2022 IEEE International Conference on Data Mining (ICDM), 1179-1184, 2022 | 1 | 2022 |
| Matrix Profile Index Approximation for Streaming Time Series M Shahcheraghi, T Cappon, S Oymak, E Papalexakis, E Keogh, ... 2021 IEEE International Conference on Big Data (Big Data), 2775-2784, 2021 | 1 | 2021 |
| Matrix Profile Index Prediction for Streaming Time Series M Shahcheraghi, T Cappon, S Oymak, E Papalexakis, E Keogh, ... Workshop on ML for Systems at NeurIPS 2020, 2020 | 1 | 2020 |
| Breaking computational barriers to perform time series pattern mining at scale and at the edge ZP Zimmerman University of California, Riverside, 2019 | 1 | 2019 |
| FPGA-based Acceleration of Time Series Similarity Prediction: From Cloud to Edge A Kalantar, Z Zimmerman, P Brisk ACM Transactions on Reconfigurable Technology and Systems 16 (1), 1-27, 2022 | | 2022 |
| The matrix profile: a fast and sensitive template matching method for seismic event detection that does not require templates NS Senobari, G Funning, Z Zimmermann, Y Zhu, PM Shearer, P Brisk, ... Authorea Preprints, 2022 | | 2022 |
Eamonn KeoghDistinguished Professor of Computer Science, University of California - RiversideVerified email at cs.ucr.edu
