Multiseries Featural LSTM for Partial Periodic Time-Series Prediction: A Case Study for Steel Industry

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2020.2967247 ◽

2020 ◽

Vol 69 (9) ◽

pp. 5994-6003

Author(s):

Tianyu Wang ◽

Henry Leung ◽

Jun Zhao ◽

Wei Wang

Keyword(s):

Time Series ◽

Steel Industry ◽

Time Series Prediction ◽

Periodic Time Series

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Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting

Measurement ◽

10.1016/j.measurement.2017.02.007 ◽

2017 ◽

Vol 103 ◽

pp. 52-61 ◽

Author(s):

Yusuf Yaslan ◽

Bahadır Bican

Keyword(s):

Time Series ◽

Support Vector Regression ◽

Time Series Prediction ◽

Load Forecasting ◽

Support Vector ◽

Denoising Method ◽

Mode Decomposition ◽

Electricity Load ◽

Electricity Load Forecasting

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An Efficient Anomaly Detection in Quasi-Periodic Time Series Data—A Case Study with ECG

Time Series Analysis and Forecasting - Contributions to Statistics ◽

10.1007/978-3-319-96944-2_10 ◽

2018 ◽

pp. 147-157 ◽

Author(s):

Goutam Chakraborty ◽

Takuya Kamiyama ◽

Hideyuki Takahashi ◽

Tetsuo Kinoshita

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Time Series Data ◽

Series Data ◽

Periodic Time Series

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Time Series Prediction of Air Pollutants : A Case Study for Serbia, Bosnia and Herzegovina and Italy

2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) ◽

10.1109/infoteh.2019.8717778 ◽

2019 ◽

Author(s):

Radmila Jankovic ◽

Marijana Cosovic ◽

Alessia Amelio

Keyword(s):

Time Series ◽

Bosnia And Herzegovina ◽

Air Pollutants ◽

Time Series Prediction

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A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin

Earth Science Informatics ◽

10.1007/s12145-020-00477-2 ◽

2020 ◽

Vol 13 (3) ◽

pp. 915-927 ◽

Author(s):

Dostdar Hussain ◽

Tahir Hussain ◽

Aftab Ahmed Khan ◽

Syed Ali Asad Naqvi ◽

Akhtar Jamil

Keyword(s):

Time Series ◽

Deep Learning ◽

River Basin ◽

Time Series Prediction ◽

Learning Approach

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Deep Learning Models Performance For NDVI Time Series Prediction: A Case Study On North West Tunisia

2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS) ◽

10.1109/m2garss47143.2020.9105149 ◽

2020 ◽

Author(s):

Manel Rhif ◽

Ali Ben Abbes ◽

Beatriz Martinez ◽

Imed Riadh Farah

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Prediction ◽

Learning Models ◽

Ndvi Time Series

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Time series prediction using parametric models and multilayer perceptrons: case study on heart signals

2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). ◽

10.1109/icassp.2003.1202481 ◽

2003 ◽

Author(s):

R. El Dajani ◽

M. Miquel ◽

P. Maison-Blanche ◽

P. Rubel

Keyword(s):

Time Series ◽

Time Series Prediction ◽

Parametric Models ◽

Multilayer Perceptrons

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Memetic Approaches for Optimizing Hidden Markov Models: A Case Study in Time Series Prediction

Artificial Life: Borrowing from Biology - Lecture Notes in Computer Science ◽

10.1007/978-3-642-10427-5_26 ◽

2009 ◽

pp. 262-271

Author(s):

Lam Thu Bui ◽

Michael Barlow

Keyword(s):

Time Series ◽

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov ◽

Time Series Prediction

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Time Series Prediction Based on Facebook Prophet: A Case Study, Temperature Forecasting in Myintkyina

International Journal of Applied Mathematics Electronics and Computers ◽

10.18100/ijamec.816894 ◽

2020 ◽

pp. 263-267

Author(s):

Zar Zar OO ◽

Sabai PHYU

Keyword(s):

Time Series ◽

Time Series Prediction ◽

Temperature Forecasting

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Building high performant classifiers by Integrating bayesian learning, mutual Information and committee techniques — A case study in time series prediction —

Lecture Notes in Computer Science - Artificial Neural Networks — ICANN'97 ◽

10.1007/bfb0020287 ◽

1997 ◽

pp. 1023-1028 ◽

Author(s):

Thomas Ragg ◽

Steffen Gutjahr

Keyword(s):

Time Series ◽

Mutual Information ◽

Bayesian Learning ◽

Time Series Prediction

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Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil

Applied Energy ◽

10.1016/j.apenergy.2018.05.043 ◽

2018 ◽

Vol 224 ◽

pp. 550-566 ◽

Author(s):

A. Khosravi ◽

L. Machado ◽

R.O. Nunes

Keyword(s):

Machine Learning ◽

Time Series ◽

Wind Farm ◽

Time Series Prediction ◽

Learning Algorithms ◽

Machine Learning Algorithms

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