Reducing forecast errors by logarithmic transformations for complex time series

Yang, Zhengling; Wang, Teng; Duan, Zhifeng; Zhang, Jun

doi:10.1109/cecnet.2012.6201999

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, nonlinear theories and Fourier analysis confirm the low-order nonlinear transformation has three merits as follows [7]. i.…”

Section: Low-order Nonlinear Transformationmentioning

confidence: 96%

Improving Forecasting Performance of Back Propagation to Wind Speed by Square Root Transformation

Gao

2013

AMM

View full text Add to dashboard Cite

In order to improve the forecast accuracy and reliability of wind speed with strong randomness, this paper suggests a method. First, apply square root transformation to the wind speed time series, which is different from familiar normalization. Second, BP neural network is employed to forecast the future wind speed time series. Finally, invert the forecasted time series by the inverse square root transformation. The experiment result shows the effectiveness of the proposed method.

show abstract

“…Besides, nonlinear theories and Fourier analysis confirm the low-order nonlinear transformation has three merits as follows [7]. i.…”

Section: Low-order Nonlinear Transformationmentioning

confidence: 96%

Improving Forecasting Performance of Back Propagation to Wind Speed by Square Root Transformation

Gao

2013

AMM

View full text Add to dashboard Cite

show abstract

Joint Optimization Strategy of Condition-Based Maintenance and Spare Parts Ordering for Nonlinear Degraded Equipment under Imperfect Maintenance

Yang

Pei

et al. 2022

Machines

View full text Add to dashboard Cite

When existing methods consider the impact of the imperfect maintenance activities on random degraded equipment, they usually assume that the degraded equipment is linear, and set the number of imperfect maintenance activities in advance. However, in engineering practice, most of the degraded equipment is nonlinear, and the number of imperfect maintenance activities has an impact on the development of maintenance strategies. Therefore, this paper proposes a new joint maintenance strategy considering the uncertainty of the number of imperfect maintenance activities for nonlinear degraded equipment. First, nonlinear degradation data are linearized based on Box–Cox transformation (BCT), and the degradation model under the influence of imperfect activities is constructed by the random coefficient regression model. Accordingly, the remaining useful life (RUL) probability distribution can be derived. Secondly, the number of maintenances is calculated by imperfect maintenance level and success probability. Then, the detection cycle, preventive maintenance threshold and maintenance times are taken as decision variables. The optimization objective is to minimize the expected average cost. However, this is restricted by availability and probability of success for imperfect maintenance activities. A multi-objective joint optimization model of condition-based maintenance and spare parts ordering is constructed. Finally, the analysis results based on numerical examples verify the feasibility of the proposed joint optimization strategy.

show abstract

Reducing forecast errors by logarithmic transformations for complex time series

Cited by 2 publications

References 8 publications

Improving Forecasting Performance of Back Propagation to Wind Speed by Square Root Transformation

Improving Forecasting Performance of Back Propagation to Wind Speed by Square Root Transformation

Joint Optimization Strategy of Condition-Based Maintenance and Spare Parts Ordering for Nonlinear Degraded Equipment under Imperfect Maintenance

Contact Info

Product

Resources

About