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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Learning Deep Time-Index Models For Time Series Forecasting, Jiale Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi
Learning Deep Time-Index Models For Time Series Forecasting, Jiale Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi
Research Collection School Of Computing and Information Systems
Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historicalvalue models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while naive deep timeindex models are far more expressive than the manually predefined function representations of classical time-index models, they are inadequate for forecasting, being unable to generalize to unseen time steps due to the lack of inductive bias. In this paper, we propose DeepTime, a …
Ultra-Short-Term Wind Power Forecasting Based On Ceemd And Chaos Theory, Lijie Wang, Zhang Li, Zhang Yan
Ultra-Short-Term Wind Power Forecasting Based On Ceemd And Chaos Theory, Lijie Wang, Zhang Li, Zhang Yan
Journal of System Simulation
Abstract: This paper studies the ultra-short-term prediction of wind power generating capacity by means of CEEMD and chaos theory. Wind power time series is decomposed by CEEMD to decrease the non-stationary of time series. CEEMD can overcome the modal aliasing problem of EMD. The phase space reconstruction method is used to extract characteristics of each sequence, which provides the basis for the selection of input dimension when building a model. The least squares support vector machine models are built for each sequence and the prediction are made separately. The predicted results are added to get the final prediction. Simulation is …
Ad-Hoc Automated Teller Machine Failure Forecast And Field Service Optimization, Michelle L. F. Cheong, Ping Shung Koo, B. Chandra Babu
Ad-Hoc Automated Teller Machine Failure Forecast And Field Service Optimization, Michelle L. F. Cheong, Ping Shung Koo, B. Chandra Babu
Research Collection School Of Computing and Information Systems
As part of its overall effort to maintain good customer service while managing operational efficiency and reducing cost, a bank in Singapore has embarked on using data and decision analytics methodologies to perform better ad-hoc ATM failure forecasting and plan the field service engineers to repair the machines. We propose using a combined Data and Decision Analytics Framework which helps the analyst to first understand the business problem by collecting, preparing and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to solve the problem. This paper reports the work in analyzing …