"Deep Learning-Based Approach for Time Series Forecasting with Application to Electricity Load"

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Book	Author(s)	Year	Publisher
Machine Learning for Time Series Forecasting with Python	Francesca Lazzeri PhD,	2020	John Wiley & Sons
Deep Learning for Time Series Forecasting - Predict the Future with MLPs, CNNs and LSTMs in Python	Jason Brownlee	2018
Time series and forecasting: an applied approach	Bruce L. Bowerman	1979	Duxbury Press
SAS for Forecasting Time Series	John C. Brocklebank; David A. Dickey; Bong S. Choi	2018	SAS Institute
SAS for forecasting time series /	Brocklebank, John Clare.; Dickey, David A.	[2003], ©2003.	J. Wiley,
Deep Reinforcement Learning with Guaranteed Performance: A Lyapunov-Based Approach	Yinyan Zhang; Shuai Li; Xuefeng Zhou	1st ed. 2020	Springer International Publishing
Time-Series Forecasting	Chris Chatfield	2000	Routledge
Time-Series Forecasting	Chris Chatfield	2000	Chapman and Hall/CRC
Time-series forecasting /	Chatfield, Christopher.	[2001], ©2001.	Chapman & Hall/CRC,
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	Umberto Michelucci	1st ed.	Apress
Time series and forecasting with IDA	Harry V. Roberts	1984	Scientific Press;McGraw-Hill;Distributed by McGraw-Hill
Forecasting, time series, and regression: an applied approach /	Bowerman, Bruce L.; O'Connell, Richard T.; Bowerman, Bruce L.	[2005], ©2005.	Thomson Brooks/Cole,
Automated Software Engineering: A Deep Learning-Based Approach	Suresh Chandra Satapathy; Ajay Kumar Jena; Jagannath Singh; Saurabh Bilgaiyan	1st ed. 2020	Springer International Publishing
Practical MATLAB Deep Learning: A Project-Based Approach	Michael Paluszek; Stephanie Thomas	1st ed.	Apress
Electrical Load Forecasting	Soliman Abdel-hady Soliman; Ph.D.; and Ahmad M. Al-Kandari; Ph.D.	2010	Butterworth-Heinemann
Introduction to Time Series and Forecasting	Peter J. Brockwell; Richard A. Davis	3rd ed. 2016	Springer International Publishing
Introduction to Time Series and Forecasting	Peter J. Brockwell; Richard A. Davis	2nd ed. 2002	Springer New York
Introduction to Time Series and Forecasting	Peter J. Brockwell; Richard A. Davis	1996	Springer New York
Forecasting Economic Time Series	Michael Clements; David Hendry	1998-10	Cambridge University Press
Universal Time-Series Forecasting with Mixture Predictors	Daniil Ryabko	1st ed. 2020	Springer International Publishing
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Deep Learning-Based Approach for Time Series Forecasting with Application to Electricity Load	J. F. Torres; A. M. Fernández; A. Troncoso; F. Martínez-Álvarez	Biomedical Applications Based on Natural and Artificial Computing: International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Corunna, Spain, June 19-23, 2017, Proceedings, Part II	José Manuel Ferrández Vicente; José Ramón Álvarez-Sánchez; Félix de la Paz López; Javier Toledo Moreo; Hojjat Adeli	1st ed. 2017	Springer International Publishing
Deep Learning Approach for Electricity Load Forecasting Using Multivariate Time Series Data	Shishir Zaman; Md. Nayeem; Rifah Tatrapi; Shamim Ripon	Machine Intelligence and Data Science Applications: Proceedings of MIDAS 2021	Vaclav Skala; T. P. Singh; Tanupriya Choudhury; Ravi Tomar; Md. Abul Bashar	1st ed. 2022	Springer Nature Singapore
Deep Learning-Based Time Series Forecasting	Kushagra Agarwal; Lalasa Dheekollu; Gaurav Dhama; Ankur Arora; Siddhartha Asthana; Tanmoy Bhowmik	Deep Learning Applications, Volume 3	M. Arif Wani; Bhiksha Raj; Feng Luo; Dejing Dou	1st ed. 2022	Springer Nature Singapore
Data Decomposition Based Learning for Load Time-Series Forecasting	Jatin Bedi; Durga Toshniwal	ECML PKDD 2020 Workshops: Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14–18, 2020, Proceedings	Irena Koprinska; Michael Kamp; Annalisa Appice; Corrado Loglisci; Luiza Antonie; Albrecht Zimmermann; Riccardo Guidotti; Özlem Özgöbek; Rita P. Ribeiro; Ricard Gavaldà; João Gama; Linara Adilova; Yamuna Krishnamurthy; Pedro M. Ferreira; Donato Malerba; Ibéria Medeiros; Michelangelo Ceci; Giuseppe Manco; Elio Masciari; Zbigniew W. Ras; Peter Christen; Eirini Ntoutsi; Erich Schubert; Arthur Zimek; Anna Monreale; Przemyslaw Biecek; Salvatore Rinzivillo; Benjamin Kille; Andreas Lommatzsch; Jon Atle Gulla	1st ed. 2020	Springer International Publishing
Deep Learning Approaches to Time Series Forecasting	Abhishek Sharma; Sachin Kumar Jain	Recent Advances in Time Series Forecasting	Dinesh C. S. Bisht; Mangey Ram	2021	Routledge
Electricity Load Forecasting: A Weekday-Based Approach	Irena Koprinska; Mashud Rana; Vassilios G. Agelidis	Artificial Neural Networks and Machine Learning -- ICANN 2012: 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part II	Alessandro Villa; Włodzisław Duch; Péter Érdi; Francesco Masulli; Günther Palm	2012	Springer Berlin Heidelberg
Deep Learning Approach to Load Forecasting: A Survey	Segun A. Akinola; Prabhat Thakur; Mayank S. Sharma; Krishna Kumar; Ghanshyam Singh	Communication, Networks and Computing: Second International Conference, CNC 2020, Gwalior, India, December 29–31, 2020, Revised Selected Papers	Ranjeet Singh Tomar; Shekhar Verma; Brijesh Kumar Chaurasia; Vrijendra Singh; Jemal Abawajy; Shyam Akashe; Pao-Ann Hsiung; Vijay K. Bhargava	1st ed. 2021	Springer Nature Singapore
Time Series Forecasting Using Deep Learning		MATLAB Deep Learning Toolbox™ User's Guide	The MathWorks, Inc.	2020	The MathWorks, Inc.
Chapter 13: Deep Learning for Time Series Forecasting		Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation	Tarek A. Atwan	2022	Packt Publishing
Promise of Deep Learning for Time Series Forecasting		Deep Learning for Time Series Forecasting - Predict the Future with MLPs, CNNs and LSTMs in Python	Jason Brownlee	2018
Operational Flexibility with Statistical and Deep Learning Model for Electricity Load Forecasting	Ayush Sinha; Raghav Tayal; Ranjana Vyas; O. P. Vyas	International Conference on Artificial Intelligence and Sustainable Engineering: Select Proceedings of AISE 2020, Volume 2	Goutam Sanyal; Carlos M. Travieso-González; Shashank Awasthi; Carla M.A. Pinto; B. R. Purushothama	1st ed. 2022	Springer Nature Singapore
Deep Learning Approach to Time Series Modeling		Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python	Hariom Tatsat, Sahil Puri, Brad Lookabaugh	2020	O'Reilly Media
Electricity load forecasting		Power system planning technologies and applications: concepts, solutions and management	Fawwaz Elkarmi	2012	Engineering Science Reference
Deep learning for time series		Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods	Ben Auffarth	2021	Packt Publishing - ebooks Account
Deep Learning for Time Series	Taesam Lee; Vijay P. Singh; Kyung Hwa Cho	Deep Learning for Hydrometeorology and Environmental Science	Taesam Lee; Vijay P. Singh; Kyung Hwa Cho	1st ed. 2021	Springer International Publishing
A Survey on Deep Learning for Time-Series Forecasting	Amal Mahmoud; Ammar Mohammed	Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges	Aboul Ella Hassanien; Ashraf Darwish	1st ed. 2021	Springer International Publishing
Multivariate Deep Learning Model with Ensemble Pruning for Time Series Forecasting	Mohit Kosuri; Cherry Tandu; Sobhan Sarkar; J. Maiti	Proceedings of the Seventh International Conference on Mathematics and Computing: ICMC 2021	Debasis Giri; Kim-Kwang Raymond Choo; Saminathan Ponnusamy; Weizhi Meng; Sedat Akleylek; Santi Prasad Maity	1st ed. 2022	Springer Nature Singapore
Improvement for Time Series Clustering with the Deep Learning Approach	Do Quang Dat; Phan Duy Hung	Cooperative Design, Visualization, and Engineering: 18th International Conference, CDVE 2021, Virtual Event, October 24–27, 2021, Proceedings	Yuhua Luo	1st ed. 2021	Springer International Publishing
Financial Time Series Forecasting Applying Deep Learning Algorithms	Erik Solís; Sherald Noboa; Erick Cuenca	Information and Communication Technologies: 9th Conference of Ecuador, TICEC 2021, Guayaquil, Ecuador, November 24–26, 2021, Proceedings	Juan Pablo Salgado Guerrero; Janneth Chicaiza Espinosa; Mariela Cerrada Lozada; Santiago Berrezueta-Guzman	1st ed. 2021	Springer International Publishing
Financial Time Series Forecasting Using Deep Learning Network	Preeti; Ankita Dagar; Rajni Bala; Ram Pal Singh	Applications of Computing and Communication Technologies: First International Conference, ICACCT 2018, Delhi, India, March 9, 2018, Revised Selected Papers	Ganesh Chandra Deka; Omprakash Kaiwartya; Pooja Vashisth; Priyanka Rathee	1st ed. 2018	Springer Nature Singapore
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