Handling Estimation Uncertainty with Bootstrapping: Empirical Evaluation in the Context of Hybrid Prediction Methods

The stock market is a chaotic, complex, and dynamic financial market. The prediction of future stock prices is a concern and controversial research issue for researchers. More and more analysis and prediction methods are proposed by researchers. We proposed a hybrid method for the prediction of future stock prices using LSTM and ensemble EMD in this paper. We use comprehensive EMD to decompose the complex original stock price time series into several subsequences which are smoother, more regular and stable than the original time series. Then, we use the LSTM method to train and predict each subsequence. Finally, we obtained the prediction values of the original stock price time series by fused the prediction values of several subsequences. In the experiment, we selected five data to fully test the performance of the method. The comparison results with the other four prediction methods show that the predicted values show higher accuracy. The hybrid prediction method we proposed is effective and accurate in future stock price prediction. Hence, the hybrid prediction method has practical application and reference value.

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Hybrid Prediction Method for ECG Signals Based on VMD, PSR, and RBF Neural Network

BioMed Research International ◽

10.1155/2021/6624298 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Fuying Huang ◽

Tuanfa Qin ◽

Limei Wang ◽

Haibin Wan

Keyword(s):

Neural Network ◽

Prediction Accuracy ◽

Rbf Neural Network ◽

Prediction Method ◽

Prediction Methods ◽

Ecg Signal ◽

Intrinsic Mode Functions ◽

Hybrid Prediction ◽

Ecg Signals ◽

Input Parameters

To explore a method to predict ECG signals in body area networks (BANs), we propose a hybrid prediction method for ECG signals in this paper. The proposed method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network to predict an ECG signal. To reduce the nonstationarity and randomness of the ECG signal, we use VMD to decompose the ECG signal into several intrinsic mode functions (IMFs) with finite bandwidth, which is helpful to improve the prediction accuracy. The input parameters of the RBF neural network affect the prediction accuracy and computational burden. We employ PSR to optimize input parameters of the RBF neural network. To evaluate the prediction performance of the proposed method, we carry out many simulation experiments on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the root mean square error (RMSE) and mean absolute error (MAE) of the proposed method are of 10-3 magnitude, while the RMSE and MAE of some competitive prediction methods are of 10-2 magnitude. Compared with other several prediction methods, our method obviously improves the prediction accuracy of ECG signals.

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Application of the Fuzzy Classification for Linear Hybrid Prediction Methods

Modeling and Analysis of Information Systems ◽

10.18255/1818-1015-2013-3-108-120 ◽

2015 ◽

Vol 20 (3) ◽

pp. 108-120

Author(s):

A. S. Taskin ◽

E. M. Mirkes ◽

N. Y. Sirotinina

Keyword(s):

Fuzzy Classification ◽

Prediction Methods ◽

Hybrid Prediction

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Learning and Reasoning with Action-Related Places for Robust Mobile Manipulation

Journal of Artificial Intelligence Research ◽

10.1613/jair.3451 ◽

2012 ◽

Vol 43 ◽

pp. 1-42 ◽

Cited By ~ 24

Author(s):

F. Stulp ◽

A. Fedrizzi ◽

L. Mösenlechner ◽

M. Beetz

Keyword(s):

State Estimation ◽

Empirical Evaluation ◽

Mobile Manipulation ◽

Task Context ◽

Estimation Uncertainty ◽

Experience Based Learning ◽

New Information ◽

Specific Goal ◽

The Face ◽

Initial Knowledge

We propose the concept of Action-Related Place (ARPlace) as a powerful and flexible representation of task-related place in the context of mobile manipulation. ARPlace represents robot base locations not as a single position, but rather as a collection of positions, each with an associated probability that the manipulation action will succeed when located there. ARPlaces are generated using a predictive model that is acquired through experience-based learning, and take into account the uncertainty the robot has about its own location and the location of the object to be manipulated. When executing the task, rather than choosing one specific goal position based only on the initial knowledge about the task context, the robot instantiates an ARPlace, and bases its decisions on this ARPlace, which is updated as new information about the task becomes available. To show the advantages of this least-commitment approach, we present a transformational planner that reasons about ARPlaces in order to optimize symbolic plans. Our empirical evaluation demonstrates that using ARPlaces leads to more robust and efficient mobile manipulation in the face of state estimation uncertainty on our simulated robot.

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