Souhaib Ben Taieb : Citation Profile


Are you Souhaib Ben Taieb?

Monash University

3

H index

1

i10 index

39

Citations

RESEARCH PRODUCTION:

3

Articles

4

Papers

RESEARCH ACTIVITY:

   4 years (2011 - 2015). See details.
   Cites by year: 9
   Journals where Souhaib Ben Taieb has often published
   Relations with other researchers
   Recent citing documents: 22.    Total self citations: 2 (4.88 %)

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   Permalink: http://citec.repec.org/pbe791
   Updated: 2019-09-14    RAS profile: 2015-12-20    
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Relations with other researchers


Works with:

Hyndman, Rob (3)

Authors registered in RePEc who have co-authored more than one work in the last five years with Souhaib Ben Taieb.

Is cited by:

Hong, Tao (5)

Fritsche, Ulrich (2)

Zhao, Weigang (2)

Pierdzioch, Christian (2)

Van den Poel, Dirk (1)

Hurn, Stan (1)

McCarthy, Ian (1)

Clements, Adam (1)

Taeihagh, Araz (1)

Shukla, Prashant (1)

Kotchoni, Rachidi (1)

Cites to:

Hyndman, Rob (4)

Chevillon, Guillaume (3)

Teräsvirta, Timo (3)

Galichon, Alfred (2)

Chernozhukov, Victor (2)

Paap, Richard (1)

Wohlrabe, Klaus (1)

Fan, Jianqing (1)

Kock, Anders (1)

Buchen, Teresa (1)

Yang, Lijian (1)

Main data


Where Souhaib Ben Taieb has published?


Journals with more than one article published# docs
International Journal of Forecasting3

Working Papers Series with more than one paper published# docs
Monash Econometrics and Business Statistics Working Papers / Monash University, Department of Econometrics and Business Statistics3

Recent works citing Souhaib Ben Taieb (2018 and 2017)


YearTitle of citing document
2017Financial Time Series Prediction Using Deep Learning. (2017). Navon, Ariel ; Keller, Yosi. In: Papers. RePEc:arx:papers:1711.04174.

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2019Prévisions de l’activité économique en temps de crise. (2019). Kotchoni, Rachidi ; Stevanovic, Dalibor ; Paquette-Dupuis, Manuel. In: CIRANO Project Reports. RePEc:cir:cirpro:2019rp-04.

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2019Short-term scenario-based probabilistic load forecasting: A data-driven approach. (2019). Pauwels, Eric J ; Khoshrou, Abdolrahman. In: Applied Energy. RePEc:eee:appene:v:238:y:2019:i:c:p:1258-1268.

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2019Deep learning-based feature engineering methods for improved building energy prediction. (2019). Wang, Jiayuan ; Song, Mengjie ; Zhao, Yang ; Sun, Yongjun ; Fan, Cheng. In: Applied Energy. RePEc:eee:appene:v:240:y:2019:i:c:p:35-45.

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2017Forecasting quantiles of day-ahead electricity load. (2017). Clements, Adam ; Li, Z ; Hurn, A S. In: Energy Economics. RePEc:eee:eneeco:v:67:y:2017:i:c:p:60-71.

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2018Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China. (2018). Wang, Lin ; Zeng, Yu-Rong ; Lv, Sheng-Xiang. In: Energy. RePEc:eee:energy:v:155:y:2018:i:c:p:1013-1031.

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2018Potential of three variant machine-learning models for forecasting district level medium-term and long-term energy demand in smart grid environment. (2018). Ahmad, Tanveer ; Chen, Huanxin. In: Energy. RePEc:eee:energy:v:160:y:2018:i:c:p:1008-1020.

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2018A deep learning model for short-term power load and probability density forecasting. (2018). Guo, Zhifeng ; Yang, Shanlin ; Zhang, Xiaoling ; Zhou, Kaile. In: Energy. RePEc:eee:energy:v:160:y:2018:i:c:p:1186-1200.

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2019Long-term forecast of energy commodities price using machine learning. (2019). Constantino, Michel ; Herrera, Gabriel Paes ; Naranpanawa, Athula ; Su, Jen-Je ; Pistori, Hemerson ; Tabak, Benjamin Miranda. In: Energy. RePEc:eee:energy:v:179:y:2019:i:c:p:214-221.

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2017Predicting recessions with boosted regression trees. (2017). Pierdzioch, Christian ; Fritsche, Ulrich ; Dopke, Jorg. In: International Journal of Forecasting. RePEc:eee:intfor:v:33:y:2017:i:4:p:745-759.

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2018Benchmarking robustness of load forecasting models under data integrity attacks. (2018). Hong, Tao ; Luo, Jian ; Fang, Shu-Cherng. In: International Journal of Forecasting. RePEc:eee:intfor:v:34:y:2018:i:1:p:89-104.

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2019A data-driven framework for predicting weather impact on high-volume low-margin retail products. (2019). Desmet, Bram ; Aghezzaf, El-Houssaine ; Verstraete, Gylian. In: Journal of Retailing and Consumer Services. RePEc:eee:joreco:v:48:y:2019:i:c:p:169-177.

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2019Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy. (2019). Duan, Zhu ; Han, Fengze ; Shi, Huipeng ; Li, Yanfei ; Liu, Hui. In: Renewable Energy. RePEc:eee:renene:v:135:y:2019:i:c:p:540-553.

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2017A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting. (2017). Shao, Zhen ; Zhou, Kai-Le ; Yang, Shan-Lin ; Chao, FU. In: Renewable and Sustainable Energy Reviews. RePEc:eee:rensus:v:75:y:2017:i:c:p:123-136.

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2018Robust Day-Ahead Forecasting of Household Electricity Demand and Operational Challenges. (2018). Gerossier, Alexis ; Kariniotakis, George ; Bocquet, Alexis ; Girard, Robin. In: Energies. RePEc:gam:jeners:v:11:y:2018:i:12:p:3503-:d:190888.

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2018Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices. (2018). Giovanelli, Christian ; Vyatkin, Valeriy ; Ichise, Ryutaro ; Sierla, Seppo. In: Energies. RePEc:gam:jeners:v:11:y:2018:i:7:p:1906-:d:159229.

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2019Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach. (2019). Chung, Il-Yop ; Husein, Munir. In: Energies. RePEc:gam:jeners:v:12:y:2019:i:10:p:1856-:d:231480.

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2017Crowdsourcing: a new tool for policy-making?. (2017). Taeihagh, Araz. In: Policy Sciences. RePEc:kap:policy:v:50:y:2017:i:4:d:10.1007_s11077-017-9303-3.

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2019Accurate Forecast Improvement Approach for Short Term Load Forecasting Using Hybrid Filter-Wrap Feature Selection. (2019). Ziggah, Yao Yevenyo ; Gyan, Patricia Semwaah ; Bao, Yukun ; Atuahene, Samuel. In: International Journal of Management Science and Business Administration. RePEc:mgs:ijmsba:v:5:y:2019:i:2:p:37-49.

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2018Machine learning for time series forecasting - a simulation study. (2018). Fischer, Thomas ; Treichel, Alex ; Krauss, Christopher. In: FAU Discussion Papers in Economics. RePEc:zbw:iwqwdp:022018.

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Works by Souhaib Ben Taieb:


YearTitleTypeCited
2011Conditionally dependent strategies for multiple-step-ahead prediction in local learning In: International Journal of Forecasting.
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article7
2011Conditionally dependent strategies for multiple-step-ahead prediction in local learning.(2011) In: International Journal of Forecasting.
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This paper has another version. Agregated cites: 7
article
2014A gradient boosting approach to the Kaggle load forecasting competition In: International Journal of Forecasting.
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article23
2012Recursive and direct multi-step forecasting: the best of both worlds In: Monash Econometrics and Business Statistics Working Papers.
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paper2
2014Boosting multi-step autoregressive forecasts In: Monash Econometrics and Business Statistics Working Papers.
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paper2
2015Probabilistic time series forecasting with boosted additive models: an application to smart meter data In: Monash Econometrics and Business Statistics Working Papers.
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paper0
2013Machine learning strategies for time series forecasting In: ULB Institutional Repository.
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paper5

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