Yujue Zhou, Jie Jiang, Shuang-Hua Yang, Ligang He, and Yulong Ding. MuSDRI: Multi-seasonal decomposition based recurrent imputation for time series. IEEE Sensors Journal, 21(20):23213–23223, 2021.
 Yujue Zhou, Jie Jiang, Kai Qian, Yulong Ding, Shuang-Hua Yang, and Ligang He. Graph convolutional networks based contamination source identification across water distribution networks. Process Safety and Environmental Protection, 155:317–324, 2021.
 Yujue Zhou, Jie Jiang, Shuang-Hua Yang, Ligang He, Yulong Ding, Kai Liu, Guozhong Zhu, and Yali Qing. A data distillation enhanced autoencoder for detecting anomalous gas consumption. Under review at IEEE Transactions on Knowledge and Data Engineering.
 Giovanni Fraquelli, Massimiliano Piacenza, and Davide Vannoni*. Scope and scale economies in multi-utilities: evidence from gas, water and electricity combinations. Applied Economics, 36(18):2045–2057, 2004.
 Xiuwen Yi, Xiaodu Yang, Yanyong Huang, Songyu Ke, Junbo Zhang, Tianrui Li, and Yu Zheng. Gas-theft suspect detection among boiler room users: A data-driven approach. IEEE Transactions on Knowledge and Data Engineering, 2021.
 Kai Qian, Jie Jiang, Yulong Ding, and Shuang-Hua Yang. Dlgea: a deep learning guided evolutionary algorithm for water contamination source identification. Neural Comput & Applic, pages 1–15, 2021.
 Xiaodu Yang, Xiuwen Yi, Shun Chen, Sijie Ruan, Junbo Zhang, Yu Zheng, and Tianrui Li. You are how you use: Catching gas theft suspects among diverse restaurant users. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pages 2885–2892, 2020.
 Hongfang Lu, Tom Iseley, Saleh Behbahani, and Lingdi Fu. Leakage detection techniques for oil and gas pipelines: State-of-the-art. Tunnelling and Underground Space Technology, 98:103249, 2020.
 Sook-Chin Yip, Wooi-Nee Tan, ChiaKwang Tan, Ming-Tao Gan, and KokSheik Wong. An anomaly detection framework for identifying energy theft and defective meters in smart grids. International Journal of Electrical Power & Energy Systems, 101:189–203, 2018.
 Yide Liu. Wireless sensor network applications in smart grid: recent trends and challenges. International Journal of Distributed Sensor Net- works, 8(9):492819, 2012.
 VA Jane and L Arockiam. Survey on iot data preprocessing. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(9): 238–244, 2021.
 Yonghong Luo, Ying Zhang, Xiangrui Cai, and Xiaojie Yuan. E2gan: End-to-end generative adversarial network for multivariate time series imputation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, pages 3094–3100. AAAI Press, 2019.
 Yassine Himeur, Khalida Ghanem, Abdullah Alsalemi, Faycal Bensaali, and Abbes Amira. Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives. Applied Energy, 287:116601, 2021.
 Pal-Stefan Murvay and Ioan Silea. A survey on gas leak detection and localization techniques. Journal of Loss Prevention in the Process Industries, 25(6):966–973, 2012.
 Zukang Hu, Beiqing Chen, Wenlong Chen, Debao Tan, and Dingtao Shen. Review of model-based and data-driven approaches for leak detection and location in water distribution systems. Water Supply, 21(7):3282–3306, 2021.
 Andrew T Hudak, Nicholas L Crookston, Jeffrey S Evans, David E Hall, and Michael J Falkowski. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from lidar data. Remote Sensing of Environment, 112(5):2232–2245, 2008.
 George EP Box, Gwilym M Jenkins, and Gregory C Reinsel. Time series analysis: forecasting and control, volume 734. John Wiley & Sons, 2011.
 Ian R White, Patrick Royston, and Angela M Wood. Multiple imputation using chained equations: issues and guidance for practice. Statistics in medicine, 30(4):377–399, 2011.
 Wei Cao, Dong Wang, Jian Li, Hao Zhou, Lei Li, and Yitan Li. Brits: Bidirectional recurrent imputation for time series. In Advances in Neural Information Processing Systems, pages 6775–6785, 2018.
 Lorenzo Baldacci, Matteo Golfarelli, Davide Lombardi, and Franco Sami. Natural gas consumption forecasting for anomaly detection. Expert systems with applications, 62:190–201, 2016.
 Hermine N Akouemo and Richard J Povinelli. Probabilistic anomaly detection in natural gas time series data. International Journal of Forecasting, 32(3):948–956, 2016.
 Lian Sun, Hexiang Yan, Kunlun Xin, and Tao Tao. Contamination source identification in water distribution networks using convolutional neural network. Environ Sci Pollut Res, (26):36786–36797, 2019.
 R P ́erez, V Puig, J Pascual, A Peralta, E Landeros, and Ll Jordanas. Pressure sensor distribution for leak detection in barcelona water distribution network. Water science and technology: water supply, 9(6): 715–721, 2009.
 Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo. Convolutional lstm network: A machine learning approach for precipitation nowcasting. In Advances in neural information processing systems, pages 802–810, 2015.
 JM Bright, CJ Smith, PG Taylor, and R Crook. Stochastic generation of synthetic minutely irradiance time series derived from mean hourly weather observation data. Solar Energy, 115:229–242, 2015.
 Cristo ́bal Esteban, Stephanie L Hyland, and Gunnar Ra ̈tsch. Real-valued (medical) time series generation with recurrent conditional gans. arXivpreprint arXiv:1706.02633, 2017.
 Junbo Zhang, Yu Zheng, and Dekang Qi. Deep spatio-temporal residual networks for citywide crowd flows prediction. In Thirty-first AAAI conference on artificial intelligence, 2017.
 Andrew A Cook, Go ̈ksel Mısırlı, and Zhong Fan. Anomaly detection for iot time-series data: A survey. IEEE Internet of Things Journal, 7(7): 6481–6494, 2019.
 Hossein Hassani, Andreia Dionisio, and Mansoureh Ghodsi. The effect of noise reduction in measuring the linear and nonlinear dependency of financial markets. Nonlinear Analysis: Real World Applications, 11(1): 492–502, 2010.
 Dal Ahn, J-S Park, C-S Kim, Juno Kim, Yongxi Qian, and Tatsuo Itoh. A design of the low-pass filter using the novel microstrip defected ground structure. IEEE transactions on microwave theory and techniques, 49(1): 86–93, 2001.
 Gaihua Wang, Dehua Li, Weimin Pan, and Zhaoxiang Zang. Modified switching median filter for impulse noise removal. Signal Processing, 90(12):3213–3218, 2010.
 Dah-Jing Jwo and Ta-Shun Cho. Critical remarks on the linearised and extended kalman filters with geodetic navigation examples. Measurement, 43(9):1077–1089, 2010.
 David L Donoho and Jain M Johnstone. Ideal spatial adaptation by wavelet shrinkage. biometrika, 81(3):425–455, 1994.
 Min Han, Yuhua Liu, Jianhui Xi, and Wei Guo. Noise smoothing for nonlinear time series using wavelet soft threshold. IEEE signal processing letters, 14(1):62–65, 2006.
 Anjana Francis and C Muruganantham. An adaptive denoising method using empirical wavelet transform. International Journal of Computer Applications, 117(21), 2015.
 Santosh Kumar Yadav, Rohit Sinha, and Prabin Kumar Bora. Electro- cardiogram signal denoising using non-local wavelet transform domain filtering. IET Signal Processing, 9(1):88–96, 2015.
 Norden E Huang, Zheng Shen, Steven R Long, Manli C Wu, Hsing H Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung, and Henry H Liu. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971):903–995, 1998.
 Abdel Boudraa and Jean-Christophe Cexus. Denoising via empirical mode decomposition. Proc. IEEE ISCCSP, 4(2006), 2006.
 Patrick Flandrin, Gabriel Rilling, and Paulo Goncalves. Empirical mode decomposition as a filter bank. IEEE signal processing letters, 11(2): 112–114, 2004.
 Zhaohua Wu and Norden E Huang. Ensemble empirical mode decompostion: a noise-assisted data analysis method. Advances in adaptive data analysis, 1(01):1–41, 2009.
 John W Graham. Missing data analysis: Making it work in the real world. Annual review of psychology, 60:549–576, 2009.
 Mehmed Kantardzic. Data mining: concepts, models, methods, and algorithms. John Wiley & Sons, 2011.
 Mehran Amiri and Richard Jensen. Missing data imputation using fuzzy-rough methods. Neurocomputing, 205:152–164, 2016.
 Archana Purwar and Sandeep Kumar Singh. Hybrid prediction model with missing value imputation for medical data. Expert Systems with Applications, 42(13):5621–5631, 2015.
 Aoqian Zhang, Shaoxu Song, Yu Sun, and Jianmin Wang. Learning individual models for imputation. In 2019 IEEE 35th International Conference on Data Engineering (ICDE), pages 160–171. IEEE, 2019.
 John Galbraith and Victoria Zinde-Walsh. Autoregression-based estimators for arfima models. 2001.
 Co ̧skun Hamzac ̧ebi. Improving artificial neural networks’ performance in seasonal time series forecasting. Information Sciences, 178(23):4550–4559, 2008.
 David S Fung. Methods for the estimation of missing values in time series. 2006.
 Eben Afrifa-Yamoah, Ute A Mueller, SM Taylor, and AJ Fisher. Missing data imputation of high-resolution temporal climate time series data. Meteorological Applications, 27(1):e1873, 2020.
 Jiali Mei, Yohann De Castro, Yannig Goude, and Georges H ́ebrail. Non-negative matrix factorization for time series recovery from a few temporal aggregates. In International Conference on Machine Learning, pages 2382–2390. PMLR, 2017.
 Xiaoxiang Song, Yan Guo, Ning Li, and Sixing Yang. A novel approach based on matrix factorization for recovering missing time series sensor data. IEEE Sensors Journal, 20(22):13491–13500, 2020.
 Yuehua Liu, Tharam Dillon, Wenjin Yu, Wenny Rahayu, and Fahed Mostafa. Missing value imputation for industrial iot sensor data with large gaps. IEEE Internet of Things Journal, 7(8):6855–6867, 2020.
 Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. Recurrent neural networks for multivariate time series with missing values. Scientific reports, 8(1):1–12, 2018.
 Jinsung Yoon, William R Zame, and Mihaela van der Schaar. Multi-directional recurrent neural networks: A novel method for estimating missing data. In Time Series Workshop in International Conference on Machine Learning, 2017.
 Yonghong Luo, Xiangrui Cai, Ying Zhang, Jun Xu, and Xiaojie Yuan. Multivariate time series imputation with generative adversarial networks. In Advances in Neural Information Processing Systems, pages 1596–1607, 2018.
 Tongge Huang, Pranamesh Chakraborty, and Anuj Sharma. Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images. arXiv preprint arXiv:2005.04188, 2020.
 Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, and Yisong Yue. Naomi: Non-autoregressive multiresolution sequence imputation. arXiv preprint arXiv:1901.10946, 2019.
 Vincent Fortuin, Dmitry Baranchuk, Gunnar Ra ̈tsch, and Stephan Mandt. Gp-vae: Deep probabilistic time series imputation. In InternationalConference on Artificial Intelligence and Statistics, pages 1651–1661. PMLR, 2020.
 Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali, and Abbes Amira. A novel approach for detecting anomalous energy consumption based on micro-moments and deep neural networks. Cognitive Computation, 12 (6):1381–1401, 2020.
 M Dilraj, K Nimmy, and Sriram Sankaran. Towards behavioral profiling based anomaly detection for smart homes. In TENCON 2019-2019 IEEE Region 10 Conference (TENCON), pages 1258–1263. IEEE, 2019.
 Haroon Rashid, Nipun Batra, and Pushpendra Singh. Rimor: Towards identifying anomalous appliances in buildings. In Proceedings of the 5thConference on Systems for Built Environments, pages 33–42, 2018.
 Haroon Rashid, Vladimir Stankovic, Lina Stankovic, and Pushpen- dra Singh. Evaluation of non-intrusive load monitoring algorithms for appliance-level anomaly detection. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8325–8329. IEEE, 2019.
 Wilhelm Kleiminger, Christian Beckel, Thorsten Staake, and Silvia Santini. Occupancy detection from electricity consumption data. In Proceedings of the 5th ACM Workshop on Embedded Systems For Energy- Efficient Buildings, pages 1–8, 2013.
 Adnan Akbar, Michele Nati, Francois Carrez, and Klaus Moessner. Con- textual occupancy detection for smart office by pattern recognition of electricity consumption data. In 2015 IEEE international conference on communications (ICC), pages 561–566. IEEE, 2015.
 Soma Shekara Sreenadh Reddy Depuru, Lingfeng Wang, and Vijay Dev- abhaktuni. Support vector machine based data classification for detection of electricity theft. In 2011 IEEE/PES Power Systems Conference and Exposition, pages 1–8. IEEE, 2011.
 A Amara Korba and N El Islem Karabadji. Smart grid energy fraud detection using svm. In international Conference on Networking and Advanced Systems (ICNAS), 2019.
 Sonal Jain, Kushan A Choksi, and Naran M Pindoriya. Rule-based classification of energy theft and anomalies in consumers load demand profile. IET Smart Grid, 2(4):612–624, 2019.
 Luigi Patrono, Patrizio Primiceri, Piercosimo Rametta, Ilaria Sergi, and Paolo Visconti. An innovative approach for monitoring elderly behavior by detecting home appliance’s usage. In 2017 25th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pages 1–7. IEEE, 2017.
 Paolo Visconti, Paolo Costantini, Roberto de Fazio, Aim`e Lay-Ekuakille, and Luigi Patrono. A sensors-based monitoring system of electrical consumptions and home parameters remotely managed by mobile app for elderly habits’ control. In 2019 IEEE 8th International Workshop on Advances in Sensors and Interfaces (IWASI), pages 264–269. IEEE, 2019.
 National Bureau of Statistics. http://www.stats.gov.cn/tjsj/, 2021.
 Lingya Meng, Li Yuxing, Wang Wuchang, and Fu Juntao. Experimental study on leak detection and location for gas pipeline based on acoustic method. Journal of Loss Prevention in the Process Industries, 25(1): 90–102, 2012.
 Shuaiyong Li, Yumei Wen, Ping Li, Jin Yang, Xiaoxuan Dong, and Yanhua Mu. Leak location in gas pipelines using cross-time–frequency spectrum of leakage-induced acoustic vibrations. Journal of Sound andVibration, 333(17):3889–3903, 2014.
 Darryl G Murdock, Steven V Stearns, R Todd Lines, Dawn Lenz, David M Brown, and C Russell Philbrick. Applications of real-world gas detection: Airborne natural gas emission lidar (angel) system. Journal of Applied Remote Sensing, 2(1):023518, 2008.
 Chet Sandberg, Jim Holmes, Ken McCoy, and HEINRICH Koppitsch. The application of a continuous leak detection system to pipelines and associated equipment. IEEE Transactions on Industry applications, 25 (5):906–909, 1989.
 William E Lowry, Sandra Dalvit Dunn, Robert Walsh, Daniel Merewether, and Desario V Rao. Method and system to locate leaks in subsurface containment structures using tracer gases, March 14 2000. US Patent 6,035,701.
 David Cist and Alan Schutz. A low-cost gpr gas pipe & leak detector. geophysical survey system. Inc.(US), 2005.
 J CP Liou. Leak detection by mass balance effective for norman wells line. Oil and gas journal, 94(17), 1996.
 AE Liu. Overview: Pipeline accounting and leak detection by mass balance. Theory And Hardware Implementation, 2008.
 Reinaldo A Silva, Claudio M Buiatti, Sandra L Cruz, and Jo ̃ao AFR Pereira. Pressure wave behaviour and leak detection in pipelines. Com- puters & chemical engineering, 20:S491–S496, 1996.
 Huali Chen, Hao Ye, LV Chen, and Hongyu Su. Application of support vector machine learning to leak detection and location in pipelines. In Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE cat. no. 04ch37510), volume 3, pages 2273–2277. IEEE, 2004.
 Alex Souza de Joode and Andrew Hoffman. Pipeline leak detection and theft detection using rarefaction waves. In 6th pipeline technology conference, 2011.
 EJ Farmer, DF Wallis, G Edwards, and TW Kennedy. Long-term field tests completed on pipe leak detector program. Offshore, incorporating the Oilman, 51:86, 1991.
 SBM Beck, MD Curren, ND Sims, and R Stanway. Pipeline network features and leak detection by cross-correlation analysis of reflected waves. Journal of hydraulic engineering, 131(8):715–723, 2005.
 L Billmann and Rolf Isermann. Leak detection methods for pipelines. Automatica, 23(3):381–385, 1987.
 Javier Augusto Jim ́enez Cabas. Liquid transport pipeline monitoring architecture based on state estimators for leak detection and location. Master’s thesis, Universidad del Norte, 2018.
 Xiujia Tang and Dachun Yan. Pipeline leak detection method and instrument based on neural networks. Acta Scientiarum Naturalium-Universitatis Pekinensis, 33:319–327, 1997.
 Manabu Kotani, Masanori Katsura, and Seiichi Ozawa. Detection of gas leakage sound using modular neural networks for unknown environments. Neurocomputing, 62:427–440, 2004.
 Jinqiu Hu, Laibin Zhang, and Wei Liang. Detection of small leakage from long transportation pipeline with complex noise. Journal of Loss Prevention in the Process Industries, 24(4):449–457, 2011.
 Yipeng Wu and Shuming Liu. A review of data-driven approaches for burst detection in water distribution systems. Urban Water Journal, 14(9):972–983, 2017.
 Teck Kai Chan, Cheng Siong Chin, and Xionghu Zhong. Review of current technologies and proposed intelligent methodologies for water distributed network leakage detection. IEEE Access, 6:78846–78867, 2018.
 Andrew F Colombo, Pedro Lee, and Bryan W Karney. A selective literature review of transient-based leak detection methods. Journal of hydro-environment research, 2(4):212–227, 2009.
 Kazeem B Adedeji, Yskandar Hamam, Bolanle Tolulope Abe, and Ad- nan M Abu-Mahfouz. Towards achieving a reliable leakage detection and localization algorithm for application in water piping networks: An overview. IEEE Access, 5:20272–20285, 2017.
 Ranko S Pudar and James A Liggett. Leaks in pipe networks. Journal of Hydraulic Engineering, 118(7):1031–1046, 1992.
 Witness Mpesha, M Hanif Chaudhry, and Sarah L Gassman. Leak detection in pipes by frequency response method using a step excitation. Journal of hydraulic research, 40(1):55–62, 2002.
 Bruno Brunone. Transient test-based technique for leak detection in outfall pipes. Journal of water resources planning and management, 125(5):302–306, 1999.
 Francisco Javier Salguero, R Cobacho, and MA Pardo. Unreported leaks location using pressure and flow sensitivity in water distribution networks.Water Supply, 19(1):11–18, 2019.
 Lise Ferrandez-Gamot, Pierre Busson, Joaquim Blesa, Sebastian Tornil- Sin, Vicen ̧c Puig, Eric Duviella, and Adri`a Soldevila. Leak localization in water distribution networks using pressure residuals and classifiers. IFAC-PapersOnLine, 48(21):220–225, 2015.
 Adri`a Soldevila, Joaquim Blesa, Sebastian Tornil-Sin, Eric Duviella, Rosa M Fernandez-Canti, and Vicenc Puig. Leak localization in water distribution networks using a mixed model-based/data-driven approach.Control Engineering Practice, 55:162–173, 2016.
 Zheng Yi Wu, Paul Sage, and David Turtle. Pressure-dependent leak detection model and its application to a district water system. Journal of Water Resources Planning and Management, 136(1):116–128, 2010.
 James-A Goulet, Sylvain Coutu, and Ian FC Smith. Model falsification diagnosis and sensor placement for leak detection in pressurized pipe networks. Advanced Engineering Informatics, 27(2):261–269, 2013.
 Gaudence Moser, Stephanie German Paal, and Ian FC Smith. Leak detection of water supply networks using error-domain model falsification. Journal of Computing in Civil Engineering, 32(ARTICLE):04017077–1, 2018.
 Roya A Cody, Pampa Dey, and Sriram Narasimhan. Linear prediction for leak detection in water distribution networks. Journal of Pipeline Systems Engineering and Practice, 11(1):04019043, 2020.
 Dileep Kumar, Dezhan Tu, Naifu Zhu, Dibo Hou, and Hongjian Zhang. In-line acoustic device inspection of leakage in water distribution pipes based on wavelet and neural network. Journal of Sensors, 2017, 2017.
 Weirong Xu, Xiao Zhou, Kunlun Xin, Joby Boxall, Hexiang Yan, and Tao Tao. Disturbance extraction for burst detection in water distribution networks using pressure measurements. Water Resources Research, 56 (5):e2019WR025526, 2020.
 Roya Cody, Jinane Harmouche, and Sriram Narasimhan. Leak detection in water distribution pipes using singular spectrum analysis. UrbanWater Journal, 15(7):636–644, 2018.
 Mengfei Zhou, Qiang Zhang, Yunwen Liu, Xiaofang Sun, Yijun Cai, and Haitian Pan. An integration method using kernel principal component analysis and cascade support vector data description for pipeline leak detection with multiple operating modes. Processes, 7(10):648, 2019.
 Jinane Harmouche and Sriram Narasimhan. Long-term monitoring for leaks in water distribution networks using association rules mining. IEEE Transactions on Industrial Informatics, 16(1):258–266, 2019.
 Jinhai Liu, Dong Zang, Chen Liu, Yanjuan Ma, and Mingrui Fu. A leak detection method for oil pipeline based on markov feature and two-stage decision scheme. Measurement, 138:433–445, 2019.
 Roya A Cody, Bryan A Tolson, and Jeff Orchard. Detecting leaks in water distribution pipes using a deep autoencoder and hydroacoustic spectrograms. Journal of Computing in Civil Engineering, 34(2):04020001, 2020.
 Hafiz Hashim, Paraic Ryan, and Eoghan Clifford. A statistically based fault detection and diagnosis approach for non-residential building water distribution systems. Advanced Engineering Informatics, 46:101187, 2020.
 Guancheng Guo, Xipeng Yu, Shuming Liu, Xiyan Xu, Ziqing Ma, Xiaoting Wang, Yujun Huang, and Kate Smith. Novel leakage detection and localization method based on line spectrum pair and cubic interpolation search. Water Resources Management, 34(12):3895–3911, 2020.
 Akhand Rai and Jong-Myon Kim. A novel pipeline leak detection approach independent of prior failure information. Measurement, 167: 108284, 2021.
 Congcong Sun, Benjam ́ı Parellada, Vicenc ̧ Puig, and Gabriela Cembrano. Leak localization in water distribution networks using pressure and data-driven classifier approach. Water, 12(1):54, 2019.
 Stephen R Mounce, Richard B Mounce, and Joby B Boxall. Novelty detection for time series data analysis in water distribution systems using support vector machines. Journal of hydroinformatics, 13(4):672–686, 2011.
 Michele Romano, Zoran Kapelan, and Dragan Savic. Automated detection of pipe bursts and other events in water distribution systems. American Society of Civil Engineers, 2012.
 Fatma Karray, Alberto Garcia-Ortiz, Mohamed W Jmal, Abdulfattah M Obeid, and Mohamed Abid. Earnpipe: A testbed for smart water pipeline monitoring using wireless sensor network. Procedia Computer Science, 96:285–294, 2016.
 Xiaoting Wang, Guancheng Guo, Shuming Liu, Yipeng Wu, Xiyan Xu, and Kate Smith. Burst detection in district metering areas using deep learning method. Journal of Water Resources Planning and Management, 146(6):04020031, 2020.
 Donghwi Jung, Doosun Kang, Jian Liu, and Kevin Lansey. Improving the rapidity of responses to pipe burst in water distribution systems: a comparison of statistical process control methods. Journal of Hydroinformatics, 17(2):307–328, 2015.
 Da ́lia Loureiro, Conceic ̧a ̃o Amado, Andr ́e Martins, Diogo Vitorino, Aisha Mamade, and S ́ergio Teixeira Coelho. Water distribution systems flow monitoring and anomalous event detection: A practical approach. Urban Water Journal, 13(3):242–252, 2016.
 Yipeng Wu, Shuming Liu, Xue Wu, Youfei Liu, and Yisheng Guan. Burst detection in district metering areas using a data driven clustering algorithm. Water research, 100:28–37, 2016.
 Gaimei Guo and Gang Cheng. Mathematical modelling and application for simulation of water pollution accidents. Process Safety and Environmental Protection, 127:189–196, 2019. ISSN 0957-5820.
 A Preis and A Ostfeld. A contamination source identification model for water distribution system security. Engineering optimization, 39(8): 941–947, 2007.
 Lewis A Rossman. Epanet 2: Users manual. 2000.
 Praveen Vankayala, A Sankarasubramanian, S Ranji Ranjithan, and G Mahinthakumar. Contaminant source identification in water distribution networks under conditions of demand uncertainty. Environmental Forensics, 10(3):253–263, 2009.
 Xuesong Yan, Jingyu Gong, and Qinghua Wu. Pollution source intelligent location algorithm in water quality sensor networks. Neural Computing and Applications, 33(1):209–222, 2021.
 Feng Shang, James G Uber, and Marios M Polycarpou. Particle backtracking algorithm for water distribution system analysis. Journal of environmental engineering, 128(5):441–450, 2002.
 Carl D Laird, Lorenz T Biegler, Bart G van Bloemen Waanders, and Ro- scoe A Bartlett. Contamination source determination for water networks. Journal of Water Resources Planning and Management, 131(2):125–134, 2005.
 Jinhui Jeanne Huang and Edward A McBean. Data mining to identify contaminant event locations in water distribution systems. Journal ofWater Resources Planning and Management, 135(6):466–474, 2009.
 Lina Perelman and Avi Ostfeld. Bayesian networks for source intrusion detection. Journal of Water Resources Planning and Management, 139(4):426–432, 2013.
 Hui Wang and Kenneth W Harrison. Bayesian approach to contaminant source characterization in water distribution systems: adaptive sampling framework. Stochastic environmental research and risk assessment, 27 (8):1921–1928, 2013.
 Li Liu, S Ranji Ranjithan, and G Mahinthakumar. Contamination source identification in water distribution systems using an adaptive dynamic optimization procedure. Journal of Water Resources Planning and Management, 137(2):183–192, 2010.
 Xuesong Yan, Wenyin Gong, and Qinghua Wu. Contaminant source identification of water distribution networks using cultural algorithm. Concurrency and Computation: Practice and Experience, 29(24):e4230,2017.
 Xuesong Yan, Kewei Yang, Chengyu Hu, and Wenyin Gong. Pollution source positioning in a water supply network based on expensive optimization. Desalin Water Treat, 110:308–318, 2018.
 Xuesong Yan, Jing Zhao, Chengyu Hu, and Deze Zeng. Multimodal optimization problem in contamination source determination of water supply networks. Swarm and Evolutionary Computation, 47:66–71, 2019.
 Robert B Cleveland, William S Cleveland, Jean E McRae, and Irma Terpenning. Stl: A seasonal-trend decomposition. Journal of official statistics, 6(1):3–73, 1990.
 Steven M Pincus. Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88(6):2297–2301, 1991.
 Joshua S Richman and J Randall Moorman. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278(6):H2039–H2049, 2000.
 Christoph Bandt and Bernd Pompe. Permutation entropy: a natural complexity measure for time series. Physical review letters, 88(17):174102, 2002.
 Herv ́e Bourlard and Yves Kamp. Auto-association by multilayer perceptrons and singular value decomposition. Biological cybernetics, 59(4): 291–294, 1988.
 Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. Graph neural networks: A review of methods and applications. AI Open, 1: 57–81, 2020.
 Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.
 James Atwood and Don Towsley. Diffusion-convolutional neural networks. In NIPS, 2016.
 Kwei-Herng Lai, Daochen Zha, Junjie Xu, Yue Zhao, Guanchu Wang, and Xia Hu. Revisiting time series outlier detection: Definitions and benchmarks. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021. URL https: //openreview.net/forum?id=r8IvOsnHchr.
 Peter J Rousseeuw and Annick M Leroy. Robust regression and outlier detection, volume 589. John wiley & sons, 2005.
 Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal. Long short term memory networks for anomaly detection in time series. In Proceedings, volume 89, pages 89–94, 2015.
 Lo ̈ıc Bontemps, Van Loi Cao, James McDermott, and Nhien-An Le-Khac. Collective anomaly detection based on long short-term memory recurrent neural networks. In International conference on future data and security engineering, pages 141–152. Springer, 2016.
 Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh. Matrix profile i: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM), pages 1317–1322. Ieee, 2016.
 Yan Zhu, Chin-Chia Michael Yeh, Zachary Zimmerman, Kaveh Kamgar, and Eamonn Keogh. Matrix profile xi: Scrimp++: time series motif discovery at interactive speeds. In 2018 IEEE International Conference on Data Mining (ICDM), pages 837–846. IEEE, 2018.
 Junshui Ma and Simon Perkins. Time-series novelty detection using one-class support vector machines. In Proceedings of the International Joint Conference on Neural Networks, 2003., volume 3, pages 1741–1745. IEEE, 2003.
 Lifeng Shen, Zhuocong Li, and James Kwok. Timeseries anomaly detection using temporal hierarchical one-class network. Advances in Neural Information Processing Systems, 33:13016–13026, 2020.
 Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, et al. Unsupervised anomaly detection via variational auto-encoder for seasonal kpis in web applications. In Proceedings of the 2018 world wide web conference, pages 187–196, 2018.
 Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In International conference on learning representations, 2018.
 Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, and Gautam Shroff. Lstm-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148, 2016.
 Yong-Ho Yoo, Ue-Hwan Kim, and Jong-Hwan Kim. Recurrent reconstructive network for sequential anomaly detection. IEEE transactions on cybernetics, 2019.
 Lifeng Shen, Zhongzhong Yu, Qianli Ma, and James T Kwok. Time series anomaly detection with multiresolution ensemble decoding. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 9567–9575, 2021.
 Bin Zhou, Shenghua Liu, Bryan Hooi, Xueqi Cheng, and Jing Ye. Beatgan: Anomalous rhythm detection using adversarially generated time series. In IJCAI, pages 4433–4439, 2019.
 Berihun Fekade, Taras Maksymyuk, Maryan Kyryk, and Minho Jo. Probabilistic recovery of incomplete sensed data in iot. IEEE Internet of Things Journal, 5(4):2282–2292, 2017.
 Jinghan Du, Minghua Hu, and Weining Zhang. Missing data problem in the monitoring system: A review. IEEE Sensors Journal, 20(23): 13984–13998, 2020.
 Sepp Hochreiter and Ju ̈rgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
 Andrew C Harvey and Neil Shephard. 10 structural time series models. 1993.
 KDD-CUP. http://www.kdd.org/kdd2018/, 2020.
 Slawek2020hybrid. https://www.sodic.com.cn/, 2020.
 Ryan P Hafen. Local Regression Models: Advancements, applications, and new methods. Purdue University, 2010.
 Rob J Hyndman, George Athanasopoulos, Christoph Bergmeir, Gabriel Caceres, Leanne Chhay, Mitchell O’Hara-Wild, Fotios Petropoulos, Slava Razbash, Earo Wang, and Farah Yasmeen. forecast: Forecasting functions for time series and linear models. 2018.
 Chong Zhou and Randy C Paffenroth. Anomaly detection with robust deep autoencoders. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pages 665–674, 2017.
 Patrick Schober, Christa Boer, and Lothar A Schwarte. Correlation coefficients: appropriate use and interpretation. Anesthesia & Analgesia, 126(5):1763–1768, 2018.
 Hiroaki Sakoe and Seibi Chiba. Dynamic programming algorithm optimization for spoken word recognition. IEEE transactions on acoustics, speech, and signal processing, 26(1):43–49, 1978.
 John Paparrizos and Luis Gravano. k-shape: Efficient and accurate clustering of time series. In Proceedings of the 2015 ACM SIGMOD international conference on management of data, pages 1855–1870, 2015.
 James MacQueen. Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1, pages 281–297. Oakland, CA, USA, 1967.136
 Tung Kieu, Bin Yang, Chenjuan Guo, and Christian S Jensen. Outlier detection for time series with recurrent autoencoder ensembles. In IJCAI, pages 2725–2732, 2019.
 Chunyong Yin, Sun Zhang, Jin Wang, and Neal N Xiong. Anomaly detection based on convolutional recurrent autoencoder for iot time series. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(1):112–122, 2020.
 Markus Thill, Wolfgang Konen, Hao Wang, and Thomas Ba ̈ck. Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing, 112:107751, 2021.
 Jonathan Masci, Ueli Meier, Dan Cire ̧san, and Ju ̈rgen Schmidhuber. Stacked convolutional auto-encoders for hierarchical feature extraction. In International conference on artificial neural networks, pages 52–59. Springer, 2011.
 Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jo ̈rg Sander. Lof: identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pages 93–104, 2000.
 Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. Isolation-based anomaly detection. ACM Transactions on Knowledge Discovery from Data (TKDD), 6(1):1–39, 2012.
 Julien Audibert, Pietro Michiardi, Fr ́ed ́eric Guyard, S ́ebastien Marti, and Maria A Zuluaga. Usad: Unsupervised anomaly detection on multivariate time series. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 3395–3404, 2020.
 Peter J Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53–65, 1987.
 Najah Kadhim Al-Bedyry, Arumugam Sathasivan, and Afrah Jaber Al- Ithari. Ranking pipes in water supply systems based on potential to cause discolored water complaints. Process Safety and Environmental Protection, 104:517–522, 2016. ISSN 0957-5820. Challenges in Environmental Science and Engineering.
 wikipedia. 2014 Elk River chemical spill. https://en.wikipedia.org/ wiki/2014_Elk_River_chemical_spill, 2014. [Online; accessed 30- Feb-2021].
 Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. Back-propagation applied to handwritten zip code recognition. Neural computation, 1(4):541–551, 1989.
 I Lippai. Wolf-cordera ranch. http://emps.exeter.ac.uk/ engineering/research/cws/resources/benchmarks/expansion/ wolf-cordera-ranch.php Accessed February 4, 2021.
 Xingyi Cheng, Ruiqing Zhang, Jie Zhou, and Wei Xu. Deeptransport: Learning spatial-temporal dependency for traffic condition forecasting. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2018.
 Aur ́elien Delfosse, Georges Hebrail, and Aimen Zerroug. Deep learning applied to nilm: is data augmentation worth for energy disaggregation? In ECAI 2020, pages 2972–2977. IOS Press, 2020.
 Hasan Rafiq, Xiaohan Shi, Hengxu Zhang, Huimin Li, Manesh Kumar Ochani, and Aamer Abbas Shah. Generalizability improvement of deep learning-based non-intrusive load monitoring system using data augment- ation. IEEE Transactions on Smart Grid, 12(4):3265–3277, 2021.
 Odongo Steven Eyobu and Dong Seog Han. Feature representation and data augmentation for human activity classification based on wearable imu sensor data using a deep lstm neural network. Sensors, 18(9):2892, 2018.