Time Series Analysis: Forecasting and Control.

Early-warning signals for critical transitions

Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R., Dakos, V., Held, H., van Nes, E. H., Rietkerk, M., & Sugihara, G. (2009) · Nature · doi:10.1038/nature08227 · Cited by 4,854

A review on lithium-ion battery ageing mechanisms and estimations for automotive applications

Barré, A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., & Riu, D. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.05.040 · Cited by 1,758

Graph based anomaly detection and description: a survey

Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504

Battery Lifetime Prognostics

Hu, X., Xu, L., Lin, X., & Pecht, M. (2020) · Joule · doi:10.1016/j.joule.2019.11.018 · Cited by 1,320

Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)

Christ, M., Braun, N., Neuffer, J., & Kempa-Liehr, A. W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.03.067 · Cited by 1,257

Forecasting of photovoltaic power generation and model optimization: a review

Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Van Deventer, W., Horan, B., & Stojcevski, A. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.08.017 · Cited by 1,211

Solar photovoltaic generation forecasting methods: A review

Sobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018) · Energy Conversion and Management · doi:10.1016/j.enconman.2017.11.019 · Cited by 990

TS2Vec: Towards Universal Representation of Time Series

Yue, Z., Wang, Y., Duan, J., Yang, T., Huang, C., Tong, Y., & Xu, B. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20881 · Cited by 850

A survey on river water quality modelling using artificial intelligence models: 2000–2020

Tiyasha, Tung, T. M., & Yaseen, Z. M. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124670 · Cited by 650

Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0

Diez-Olivan, A., Del Ser, J., Galar, D., & Sierra, B. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.10.005 · Cited by 633

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study

Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018) · Multivariate Behavioral Research · doi:10.1080/00273171.2018.1446819 · Cited by 549

Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00765-8 · Cited by 570

On recent advances in PV output power forecast

Raza, M. Q., Nadarajah, M., & Ekanayake, C. (2016) · Solar Energy · doi:10.1016/j.solener.2016.06.073 · Cited by 544

A BIM-data mining integrated digital twin framework for advanced project management

Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103564 · Cited by 518

A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data

Babu, C. N., & Reddy, B. E. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.05.028 · Cited by 503

Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values

Cui, Z., Ke, R., Pu, Z., & Wang, Y. (2020) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2020.102674 · Cited by 486

Optimization of experimental design in fMRI: a general framework using a genetic algorithm

Wager, T. D., & Nichols, T. E. (2003) · NeuroImage · doi:10.1016/s1053-8119(02)00046-0 · Cited by 458

Past and Future Burden of Inflammatory Bowel Diseases Based on Modeling of Population-Based Data

Coward, S., Clement, F., Benchimol, E. I., Bernstein, C. N., Avina-Zubieta, J. A., Bitton, A., Carroll, M. W., Hazlewood, G., Jacobson, K., Jelinski, S., et al. (2019) · Gastroenterology · doi:10.1053/j.gastro.2019.01.002 · Cited by 449

Damage prognosis: the future of structural health monitoring

Farrar, C. R., & Lieven, N. A. J. (2007) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2006.1927 · Cited by 448

Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach

Bandara, K., Bergmeir, C., & Smyl, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112896 · Cited by 435

Revealing the dynamic network structure of the Beck Depression Inventory-II

Bringmann, L. F., Lemmens, L. H. J. M., Huibers, M. J. H., Borsboom, D., & Tuerlinckx, F. (2015) · Psychological Medicine · doi:10.1017/s0033291714001809 · Cited by 371

A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction

Chen, Y., & Hao, Y. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.02.044 · Cited by 382

Representation learning for dynamic graphs: A survey

Kazemi, S. M., Goel, R., Jain, K., Kobyzev, I., Sethi, A., Forsyth, P., & Poupart, P. (2019) · arXiv · doi:10.48550/arxiv.1905.11485 · Cited by 338

Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0)

Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A., Germann, U., & Foresti, L. (2019) · Geoscientific Model Development · doi:10.5194/gmd-12-4185-2019 · Cited by 304

Regression coefficient and autoregressive order shrinkage and selection via the lasso

Wang, H., Li, G., & Tsai, C.-L. (2007) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2007.00577.x · Cited by 304

Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model

Zhou, Y., & Huang, M. (2016) · Microelectronics Reliability · doi:10.1016/j.microrel.2016.07.151 · Cited by 285

An improved autoregressive model by particle swarm optimization for prognostics of lithium-ion batteries

Long, B., Xian, W., Jiang, L., & Liu, Z. (2013) · Microelectronics Reliability · doi:10.1016/j.microrel.2013.01.006 · Cited by 244

Socioecological transitions trigger fire regime shifts and modulate fire–climate interactions in the Sierra Nevada, USA, 1600–2015 CE

Taylor, A. H., Trouet, V., Skinner, C. N., & Stephens, S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1609775113 · Cited by 228

The Relationship Between Visual Analysis and Five Statistical Analyses in a Simple AB Single-Case Research Design

Brossart, D. F., Parker, R. I., Olson, E. A., & Mahadevan, L. (2006) · Behavior Modification · doi:10.1177/0145445503261167 · Cited by 217

Building hourly thermal load prediction using an indexed ARX model

Yun, K., Luck, R., Mago, P. J., & Cho, H. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.08.007 · Cited by 202

Improved amino acid flexibility parameters

Smith, D. K., Radivojac, P., Obradovic, Z., Dunker, A. K., & Zhu, G. (2003) · Protein Science · doi:10.1110/ps.0236203 · Cited by 164

Electrical stimulation of the ventral tegmental area induces reanimation from general anesthesia

Solt, K., Van Dort, C. J., Chemali, J. J., Taylor, N. E., Kenny, J. D., & Brown, E. N. (2014) · Anesthesiology · doi:10.1097/aln.0000000000000117 · Cited by 156

The Impact of Peer Management on Test-Ordering Behavior

Neilson, E. G., Johnson, K. B., Rosenbloom, S. T., Dupont, W. D., Talbert, D., Giuse, D. A., Kaiser, A., Miller, R. A., & and the Resource Utilization Committee* (2004) · Annals of Internal Medicine · doi:10.7326/0003-4819-141-3-200408030-00008 · Cited by 147

Statistics For Correlated Data: Phylogenies, Space, And Time

Ives, A. R., & Zhu, J. (2006) · Ecological Applications · doi:10.1890/04-0702 · Cited by 147

State-space models’ dirty little secrets: even simple linear Gaussian models can have estimation problems

Auger-Méthé, M., Field, C., Albertsen, C. M., Derocher, A. E., Lewis, M. A., Jonsen, I. D., & Mills Flemming, J. (2016) · Scientific Reports · doi:10.1038/srep26677 · Cited by 141

Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico

Johansson, M. A., Reich, N. G., Hota, A., Brownstein, J. S., & Santillana, M. (2016) · Scientific Reports · doi:10.1038/srep33707 · Cited by 120

Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey

Rojat, T., Puget, R., Filliat, D., Del Ser, J., Gelin, R., & Díaz-Rodríguez, N. (2021) · arXiv · doi:10.48550/arxiv.2104.00950 · Cited by 116

Resting-state functional magnetic resonance imaging correlates of sevoflurane-induced unconsciousness

Palanca, B. J. A., Mitra, A., Larson-Prior, L., Snyder, A. Z., Avidan, M. S., & Raichle, M. E. (2015) · Anesthesiology · doi:10.1097/aln.0000000000000731 · Cited by 112

Social vulnerability to climate in the "Little Ice Age": an example from Central Europe in the early 1770s

Pfister, C., & Brázdil, R. (2006) · Climate of the Past · doi:10.5194/cp-2-115-2006 · Cited by 111

Impact of UK policy initiatives on use of medicines to aid smoking cessation

West, R., DiMarino, M. E., Gitchell, J., & McNeill, A. (2005) · Tobacco Control · doi:10.1136/tc.2004.008649 · Cited by 92

Influence of local factors in the relationship between mortality and heat waves: Castile-La Mancha (1975–2003)

Montero, J. C., Mirón, I. J., Criado-Álvarez, J. J., Linares, C., & Díaz, J. (2012) · Science of The Total Environment · doi:10.1016/j.scitotenv.2011.10.009 · Cited by 83

Interpretable Anomaly Prediction: Predicting anomalous behavior in industry 4.0 settings via regularized logistic regression tools

Langone, R., Cuzzocrea, A., & Skantzos, N. (2020) · Data & Knowledge Engineering · doi:10.1016/j.datak.2020.101850 · Cited by 81

Increasing malaria hospital admissions in Uganda between 1999 and 2009

Okiro, E. A., Bitira, D., Mbabazi, G., Mpimbaza, A., Alegana, V. A., Talisuna, A. O., & Snow, R. W. (2011) · BMC Medicine · doi:10.1186/1741-7015-9-37 · Cited by 79

Effect of 11 September 2001 terrorist attacks in the USA on suicide in areas surrounding the crash sites

Claassen, C. A., Carmody, T., Stewart, S. M., Bossarte, R. M., Larkin, G. L., Woodward, W. A., & Trivedi, M. H. (2010) · British Journal of Psychiatry · doi:10.1192/bjp.bp.109.071928 · Cited by 74

Time Series Prediction Algorithm for Intelligent Predictive Maintenance

Lin, C.-Y., Hsieh, Y.-M., Cheng, F.-T., Huang, H.-C., & Adnan, M. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2918684 · Cited by 72

Environmental Reviews and Case Studies: Building Green Infrastructure via Citizen Participation: A Six-Year Study in the Shepherd Creek (Ohio)

Mayer, A. L., Shuster, W. D., Beaulieu, J. J., Hopton, M. E., Rhea, L. K., Roy, A. H., & Thurston, H. W. (2012) · Environmental Practice · doi:10.1017/s1466046611000494 · Cited by 72

Evidence for the effect of homes on wildfire suppression costs

Gude, P. H., Jones, K., Rasker, R., & Greenwood, M. C. (2013) · International Journal of Wildland Fire · doi:10.1071/wf11095 · Cited by 69

Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting

AghaKouchak, A., Pan, B., Mazdiyasni, O., Sadegh, M., Jiwa, S., Zhang, W., Love, C. A., Madadgar, S., Papalexiou, S. M., Davis, S. J., et al. (2022) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2021.0288 · Cited by 67

The Differential Time-Varying Effect Model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data

Jacobson, N. C., Chow, S.-M., & Newman, M. G. (2019) · Behavior Research Methods · doi:10.3758/s13428-018-1101-0 · Cited by 63

Spatio-temporal heterogeneity of malaria morbidity in Ghana: Analysis of routine health facility data

Awine, T., Malm, K., Peprah, N. Y., & Silal, S. P. (2018) · PLOS ONE · doi:10.1371/journal.pone.0191707 · Cited by 54

Time-Course Analysis of Gene Expression During the Saccharomyces cerevisiae Hypoxic Response

Bendjilali, N., MacLeon, S., Kalra, G., Willis, S. D., Hossian, A. K. M. N., Avery, E., Wojtowicz, O., & Hickman, M. J. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.116.034991 · Cited by 49

Using Bayesian belief network and time-series model to conduct prescriptive and predictive analytics for computer industries

Wang, C.-H., Cheng, H.-Y., & Deng, Y.-T. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2017.12.003 · Cited by 49

Round-trip agenda setting: tracking the intermedia process over time in the ice bucket challenge

Jang, S. M., Park, Y. J., & Lee, H. (2017) · Journalism · doi:10.1177/1464884916665405 · Cited by 31

Forecasting surface water-level fluctuations of a small glacial lake in Poland using a wavelet-based artificial intelligence method

Piasecki, A., Jurasz, J., & Adamowski, J. F. (2018) · Acta Geophysica · doi:10.1007/s11600-018-0183-5 · Cited by 30

ML for Flood Forecasting at Scale

Nevo, S., Anisimov, V., Elidan, G., El-Yaniv, R., Giencke, P., Gigi, Y., Hassidim, A., Moshe, Z., Schlesinger, M., Shalev, G., et al. (2019) · arXiv · doi:10.48550/arxiv.1901.09583 · Cited by 23

Short-term forecasts of expected deaths

Rizzi, S., & Vaupel, J. W. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025324118 · Cited by 18

How Stock Market Liquidity Impact Economic Growth in South Africa

Chipaumire, G., & Ngirande, H. (2014) · Journal of Economics · doi:10.1080/09765239.2014.11884995 · Cited by 16

Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

Casagrande, F. D., Torresen, J., & Zouganeli, E. (2019) · IEEE Access · doi:10.1109/access.2019.2933994 · Cited by 13