Time Series Analysis Forecasting and Control.

Distribution of the Estimators for Autoregressive Time Series with a Unit Root

Dickey, D. A., & Fuller, W. A. (1979) · Journal of the American Statistical Association · doi:10.1080/01621459.1979.10482531 · Cited by 23,211

Unit root tests in panel data: asymptotic and finite-sample properties

Levin, A., Lin, C.-F., & James Chu, C.-S. (2002) · Journal of Econometrics · doi:10.1016/s0304-4076(01)00098-7 · Cited by 12,849

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

Testing for causality

Granger, C. W. J. (1980) · Journal of Economic Dynamics and Control · doi:10.1016/0165-1889(80)90069-x · Cited by 1,746

Statistical and machine learning forecasting methods: Concerns and ways forward

Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018) · PLOS ONE · doi:10.1371/journal.pone.0194889 · Cited by 1,528

Recurrent Neural Networks for Time Series Forecasting: Current status and future directions

Hewamalage, H., Bergmeir, C., & Bandara, K. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.06.008 · Cited by 1,371

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

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

Handbook of frequency stability analysis

Riley, W. J., & Riley, W. J. (2008) · doi:10.6028/nist.sp.1065 · Cited by 586

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

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

Treatment of Gad: Targeting Intolerance of Uncertainty in Two Types of Worry

Dugas, M. J., & Ladouceur, R. (2000) · Behavior Modification · doi:10.1177/0145445500245002 · Cited by 334

Time Series Analysis Using Autoregressive Integrated Moving Average (ARIMA) Models

Nelson, B. K. (1998) · Academic Emergency Medicine · doi:10.1111/j.1553-2712.1998.tb02493.x · Cited by 262

Data Poisoning Attacks against Autoregressive Models

Alfeld, S., Zhu, X., & Barford, P. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10237 · Cited by 205

Generative Semi-supervised Learning for Multivariate Time Series Imputation

Miao, X., Wu, Y., Wang, J., Gao, Y., Mao, X., & Yin, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17086 · Cited by 210

Human Activity and Rest In Situ

Roenneberg, T., Keller, L. K., Fischer, D., Matera, J. L., Vetter, C., & Winnebeck, E. C. (2015) · Methods in Enzymology · doi:10.1016/bs.mie.2014.11.028 · Cited by 175

Firearm purchasing and firearm violence during the coronavirus pandemic in the United States: a cross-sectional study

Schleimer, J. P., McCort, C. D., Shev, A. B., Pear, V. A., Tomsich, E., De Biasi, A., Buggs, S., Laqueur, H. S., & Wintemute, G. J. (2021) · Injury Epidemiology · doi:10.1186/s40621-021-00339-5 · Cited by 179

Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values

Tang, X., Yao, H., Sun, Y., Aggarwal, C., Mitra, P., & Wang, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6056 · Cited by 125

Sharing and synchrony of behavior states and heart rate in nondepressed versus depressed mother-infant interactions

Field, T., Healy, B., & LeBlanc, W. G. (1989) · Infant Behavior and Development · doi:10.1016/0163-6383(89)90044-1 · Cited by 115

Randomization tests for single-case experiments: State of the art, state of the science, and state of the application

Heyvaert, M., & Onghena, P. (2014) · Journal of Contextual Behavioral Science · doi:10.1016/j.jcbs.2013.10.002 · Cited by 98

Enhanced diagnostics for the spatial analysis of field trials

Stefanova, K. T., Smith, A. B., & Cullis, B. R. (2009) · Journal of Agricultural, Biological, and Environmental Statistics · doi:10.1198/jabes.2009.07098 · Cited by 88

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

Updated 24-year trend of type 1 diabetes incidence in children in Poland reveals a sinusoidal pattern and sustained increase

Chobot, A., Polanska, J., Brandt, A., Deja, G., Glowinska‐Olszewska, B., Pilecki, O., Szadkowska, A., Mysliwiec, M., & Jarosz‐Chobot, P. (2017) · Diabetic Medicine · doi:10.1111/dme.13345 · Cited by 77

Temporal relationship between dysfunctional beliefs, self-efficacy and panic apprehension in the treatment of panic disorder with agoraphobia

Bouchard, S., Gauthier, J., Nouwen, A., Ivers, H., Vallières, A., Simard, S., & Fournier, T. (2007) · Journal of Behavior Therapy and Experimental Psychiatry · doi:10.1016/j.jbtep.2006.08.002 · Cited by 67

Adaptive Multivariate Data Compression in Smart Metering Internet of Things

Chowdhury, M. R., Tripathi, S., & De, S. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.2981382 · Cited by 62

Application of support vector regression for modeling low flow time series

Sahoo, B. B., Jha, R., Singh, A., & Kumar, D. (2019) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-018-0128-1 · Cited by 56

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

Forecasting for cruise line revenue management

Sun, X., Gauri, D. K., & Webster, S. (2011) · Journal of Revenue and Pricing Management · doi:10.1057/rpm.2009.55 · Cited by 39

City-County Government and Promises of Economic Development: A Tale of Two Cities

Carr, J. B., Bae, S.-S., & Lu, W. (2006) · State and Local Government Review · doi:10.1177/0160323x0603800301 · Cited by 30

Time Series Regression and Exploratory Data Analysis

Shumway, R. H., & Stoffer, D. S. (2017) · Springer Texts in Statistics · doi:10.1007/978-3-319-52452-8_2 · Cited by 13

Characterizing Frequency Stability Measurements Having Multiple Data Gaps

Howe, D. A., & Schlossberger, N. (2022) · IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control · doi:10.1109/tuffc.2021.3137425 · Cited by 10

Multisource and temporal variability in Portuguese hospital administrative datasets: Data quality implications

Souza, J., Caballero, I., Vasco Santos, J., Lobo, M., Pinto, A., Viana, J., Sáez, C., Lopes, F., & Freitas, A. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104242 · Cited by 7