Time-series interval prediction under uncertainty using modified double multiplicative neuron network

2021 ◽  
pp. 115478
Author(s):  
Wenping Pan ◽  
Liuyang Feng ◽  
Limao Zhang ◽  
Liang Cai ◽  
Chunlin Shen
Keyword(s):  
Time Series ◽  
Neuron Network ◽  
Interval Prediction

Interval prediction of oscillating time series based on grey system modelling

2019 ◽  
Vol 33 (2) ◽  
pp. 138
Author(s):  
Zhigang Li ◽  
Yang Zhao ◽  
Gaofei Xu ◽  
Xiaohui Wang
Keyword(s):  
Time Series ◽  
System Modelling ◽  
Grey System ◽  
Interval Prediction

scholarly journals Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition

2021 ◽  
Author(s):  
Xin Liu ◽  
Xuefeng Sang ◽  
Jiaxuan Chang ◽  
Yang Zheng
Keyword(s):  
Time Series ◽  
Water Supply ◽  
Water Demand ◽  
Coupling Model ◽  
T Test ◽  
Demand Prediction ◽  
Interval Prediction ◽  
Time Series Decomposition ◽  
Daily Water ◽  
Water Demand Prediction

Abstract The fluctuation of water supply is affected by the living habits and population mobility, so the daily water supply is significantly non-stationarity, which presents a great challenge to the water demand prediction based on data-driven model. To solve this problem, the Hodrick-Prescott (HP) and wavelet transform (WT) time series decomposition methods, and ensemble learning (EL) were introduced, coupling model bidirectional long short term memory (BLSTM), seasonal autoregressive integrated moving average (SARIMA) and Gaussian radial basis function neural network (GRBFNN) were developed, and interval prediction was carried out based on student's t-test (T-test). This research method was applied to the daily water demand prediction in Shenzhen and cross-validation was performed. It is found that the decomposed subseries has obvious law, and WT is superior to HP decomposition method. However, the maximum decomposition level (MDL) of WT should not be set too high, otherwise the trend characteristics of subseries will be weakened. The results show that the potential characteristics and quantitative relationships of historical data can be learned accurately based on WT and coupling model. Although the corona virus disease 2019 (COVID-19) outbreak in 2020 caused a variation in water supply law, this variation is still within the interval prediction. The WT and coupling model satisfactorily predicted water demand and provided the lowest mean square error (0.17%), mean relative error (0.1) and mean absolute error (3.32%) and the highest Nash-Sutcliffe efficiency (97.21%) and correlation coefficient (0.99) in testing set.

scholarly journals Deterministic and Interval Wind Speed Prediction Method in Offshore Wind Farm Considering the Randomness of Wind

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5595
Author(s):  
Qin Chen ◽  
Yan Chen ◽  
Xingzhi Bai
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Hybrid Model ◽  
Stationary Time Series ◽  
Percentage Error ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Interval Prediction ◽  
Stationary Time ◽  
Speed Prediction

In order to improve the prediction accuracy of wind speed, this paper proposes a hybrid wind speed prediction (WSP) method considering the fluctuation, randomness and nonlinear of wind, which can be applied to short-term deterministic and interval prediction. Variational mode decomposition (VMD) decomposes wind speed time series into nonlinear series Intrinsic mode function 1 (IMF1), stationary time series IMF2 and error sreies (ER). Principal component analysis-Radial basis function (PCA-RBF) model is used to model the nonlinear series IMF1, where PCA is applied to reduce the redundant information. Long short-term memory (LSTM) is used to establish a stationary time series model for IMF2, which can better describe the fluctuation trend of wind speed; mixture Gaussian process regression (MGPR) is used to predict ER to obtain deterministic and interval prediction results simultaneously. Finally, above methods are reconstructed to form VMD-PRBF-LSTM-MGPR which is the abbreviation of hybrid model to obtain the final results of WSP, which can better reflect the volatility of wind speed. Nine comparison models are built to verify the availability of the hybrid model. The mean absolute percentage error (MAE) and mean square error (MSE) of deterministic WSP of the proposed model are only 0.0713 and 0.3158 respectively, which are significantly smaller than the prediction results of comparison models. In addition, confidence intervals (CIs) and prediction interval (PIs) are compared in this paper. The experimental results show that both of them can quantify and represent forecast uncertainty and the PIs is wider than the corresponding CIs.

Interval prediction of oscillating time series based on grey system modelling

2019 ◽  
Vol 33 (2) ◽  
pp. 138
Author(s):  
Gaofei Xu ◽  
Xiaohui Wang ◽  
Zhigang Li ◽  
Yang Zhao
Keyword(s):  
Time Series ◽  
System Modelling ◽  
Grey System ◽  
Interval Prediction
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