Time Series Analysis, Forecasting and Control.

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

Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation

Erbs, D. G., Klein, S. A., & Duffie, J. A. (1982) · Solar Energy · doi:10.1016/0038-092x(82)90302-4 · Cited by 1,529

Long memory relationships and the aggregation of dynamic models

Granger, C. W. J. (1980) · Journal of Econometrics · doi:10.1016/0304-4076(80)90092-5 · Cited by 1,448

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

Assessing the impact of planned social change

Campbell, D. T. (1979) · Evaluation and Program Planning · doi:10.1016/0149-7189(79)90048-x · Cited by 957

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

Metrics and laws of software evolution-the nineties view

Lehman, M. M., Ramil, J. F., Wernick, P. D., Perry, D. E., & Turski, W. M. (2002) · Proceedings Fourth International Software Metrics Symposium · doi:10.1109/metric.1997.637156 · Cited by 561

Electric load forecasting methods: Tools for decision making

Hahn, H., Meyer-Nieberg, S., & Pickl, S. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.01.062 · Cited by 549

An optimized model using LSTM network for demand forecasting

Abbasimehr, H., Shabani, M., & Yousefi, M. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106435 · Cited by 538

Financial time series forecasting using independent component analysis and support vector regression

Lu, C.-J., Lee, T.-S., & Chiu, C.-C. (2009) · Decision Support Systems · doi:10.1016/j.dss.2009.02.001 · Cited by 537

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

Time series forecasting of solar power generation for large-scale photovoltaic plants

Sharadga, H., Hajimirza, S., & Balog, R. S. (2020) · Renewable Energy · doi:10.1016/j.renene.2019.12.131 · Cited by 441

Algorithmic financial trading with deep convolutional neural networks: Time series to image conversion approach

Sezer, O. B., & Ozbayoglu, A. M. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.04.024 · Cited by 432

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

Study and analysis of SARIMA and LSTM in forecasting time series data

Kumar Dubey, A., Kumar, A., García-Díaz, V., Kumar Sharma, A., & Kanhaiya, K. (2021) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2021.101474 · Cited by 357

HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems

Kim, J., & Kasabov, N. (1999) · Neural Networks · doi:10.1016/s0893-6080(99)00067-2 · Cited by 351

Sandpiper: Black-box and gray-box resource management for virtual machines

Wood, T., Shenoy, P., Venkataramani, A., & Yousif, M. (2009) · Computer Networks · doi:10.1016/j.comnet.2009.04.014 · Cited by 324

Dynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting

Han, L., Du, B., Sun, L., Fu, Y., Lv, Y., & Xiong, H. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467275 · Cited by 281

Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model

Yaseen, Z. M., Ebtehaj, I., Bonakdari, H., Deo, R. C., Danandeh Mehr, A., Mohtar, W. H. M. W., Diop, L., El-shafie, A., & Singh, V. P. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.09.007 · Cited by 267

A multiple-kernel support vector regression approach for stock market price forecasting

Yeh, C.-Y., Huang, C.-W., & Lee, S.-J. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.08.004 · Cited by 255

Analysis of an adaptive time-series autoregressive moving-average (ARMA) model for short-term load forecasting

Chen, J.-F., Wang, W.-M., & Huang, C.-M. (1995) · Electric Power Systems Research · doi:10.1016/0378-7796(95)00977-1 · Cited by 253

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

Covariance between psychological and endocrine responses to pharmacological challenge and psychosocial stress: a question of timing

Schlotz, W., Kumsta, R., Layes, I., Entringer, S., Jones, A., & Wüst, S. (2008) · Psychosomatic Medicine · doi:10.1097/psy.0b013e3181810658 · Cited by 237

Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

Micah, A. E., Bhangdia, K., Cogswell, I. E., Lasher, D., Lidral-Porter, B., Maddison, E. R., Nguyen, T. N. N., Patel, N., Pedroza, P., Solorio, J., et al. (2023) · The Lancet Global Health · doi:10.1016/s2214-109x(23)00007-4 · Cited by 222

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

Stock Market Prediction with High Accuracy using Machine Learning Techniques

Bansal, M., Goyal, A., & Choudhary, A. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.12.028 · Cited by 108

Neural network approach to forecasting of quasiperiodic financial time series

Bodyanskiy, Y., & Popov, S. (2006) · European Journal of Operational Research · doi:10.1016/j.ejor.2005.02.012 · Cited by 100

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

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

Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets

Bekiros, S. D. (2010) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.04.015 · Cited by 53

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