Fast prediction for sparse time series: Demand forecast of EV charging stations for cell phone applications

IEEE Transactions on Industrial Informatics

Volumn 11, Issue 1, 2015, Pages 242-250

  Majidpour, Mostafa ^a   Qiu, Charlie ^a   Chu, Peter ^a   Gadh, Rajit ^a   Pota, Hemanshu R ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Cellphone Applications; Electric Vehicles; Kernel methods; Nearest Neighbor Searches; Prediction Methods; Sparse time series

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHARGING (BATTERIES); DATA HANDLING; ELECTRIC VEHICLES; ENERGY UTILIZATION; FORECASTING; LEARNING SYSTEMS; MOBILE PHONES; NEAREST NEIGHBOR SEARCH; TELEPHONE SETS; TIME SERIES;

CELL PHONE; CELL PHONE APPLICATION; DISSIMILARITY MEASURES; KERNEL METHODS; NEAREST NEIGHBOR ALGORITHM; PREDICTION METHODS; TIME SERIES PREDICTION; UNIVERSITY OF CALIFORNIA , LOS ANGELES;

ALGORITHMS;

EID: 84923003976     PISSN: 15513203     EISSN: None     Source Type: Journal    
DOI: 10.1109/TII.2014.2374993     Document Type: Article

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