scholarly journals Time Series Prediction Method Based on E-CRBM

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 416
Author(s):  
Huixin Tian ◽  
Qiangqiang Xu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Target Prediction ◽  
Input Sequence ◽  
Series Data ◽  
Feature Transformation ◽  
Transformation Sequence ◽  
Prediction Sequence

To solve the problems of delayed prediction results and large prediction errors in one-dimensional time series prediction, a time series prediction method based on Error-Continuous Restricted Boltzmann Machines (E-CRBM) is proposed in this paper. This method constructs a deep conversion prediction framework, which is composed of two E-CRBMs and a neural network (NN). Firstly, the E-CRBM models of the original input sequence and the target prediction sequence are trained, respectively, to extract the time features of the two sequences. Then the NN model is used to connect and transform the time features. Secondly, the feature sequence H1 is extracted from the original input sequence of test data through E-CRBM1, which is used as input of NN to obtain feature transformation sequence H2. Finally, the target prediction sequence is obtained by reverse reconstruction of feature transformation sequence H2 through E-CRBM2. The E-CRBM in this paper introduces the residual sequence of NN feature transformation in the hidden layer of CRBM, which increases the robustness of CRBM and improves the overall prediction accuracy. The classical time series data (sunspot time series) and the actual operation data of reciprocating compressor are selected in the experiment. Compared with the traditional time series prediction method, the results verify the effectiveness of the proposed method in single-step prediction and multi-step prediction.

GRU-corr Neural Network Optimized by Improved PSO Algorithm for Time Series Prediction

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040010
Author(s):  
Shao-Pei Ji ◽  
Yu-Long Meng ◽  
Liang Yan ◽  
Gui-Shan Dong ◽  
Dong Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Series Data ◽  
Multi Scale ◽  
Network Parameters ◽  
Angle Data

Time series data from real problems have nonlinear, non-smooth, and multi-scale composite characteristics. This paper first proposes a gated recurrent unit-correction (GRU-corr) network model, which adds a correction layer to the GRU neural network. Then, a adaptive staged variation PSO (ASPSO) is proposed. Finally, to overcome the drawbacks of the imprecise selection of the GRU-corr network parameters and obtain the high-precision global optimization of network parameters, weight parameters and the hidden nodes number of GRU-corr is optimized by ASPSO, and a time series prediction model (ASPSO-GRU-corr) is proposed based on the GRU-corr optimized by ASPSO. In the experiment, a comparative analysis of the optimization performance of ASPSO on a benchmark function was performed to verify its validity, and then the ASPSO-GRU-corr model is used to predict the ship motion cross-sway angle data. The results show that, ASPSO has better optimization performance and convergence speed compared with other algorithms, while the ASPSO-GRU-corr has higher generalization performance and lower architecture complexity. The ASPSO-GRU-corr can reveal the intrinsic multi-scale composite features of the time series, which is a reliable nonlinear and non-steady time series prediction method.

scholarly journals Choosing the Appropriate Forecasting Model for Predictive Parameter Control

2014 ◽  
Vol 22 (2) ◽  
pp. 319-349 ◽  
Author(s):  
Aldeida Aleti ◽  
Irene Moser ◽  
Indika Meedeniya ◽  
Lars Grunske
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Optimal Algorithm ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Performance Data ◽  
Series Data ◽  
Prediction Methods ◽  
Parameter Control ◽  
Parameter Values

All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

scholarly journals A hybrid elman neural network predictor for time series prediction

2018 ◽  
Vol 7 (2.20) ◽  
pp. 159 ◽  
Author(s):  
N Mohana Sundaram ◽  
S N. Sivanandam
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Delay ◽  
Adaptive Learning ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Series Data ◽  
Elman Neural Network ◽  
Dynamic Processing ◽  
Elman Recurrent Neural Network

Artificial Neural Networks have become popular in the world of prediction and forecasting due to their nonlinear nonparametric adaptive-learning property. They become an important tool in data analysis and data mining applications. Elman neural network due to its recurrent nature and dynamic processing capabilities can perform the prediction process with a good range of accuracy. In this paper an Elman recurrent Neural Network is hybridised with a time delay called a tap delay line for time series prediction process to improve its performance. The Elman neural network with the time delay inputs is trained tested and validated using the solar sun spot time series data that contains the monthly mean sunspot numbers for a 240 year period having 2899 data values. The results confirm that the proposed Elman network hybridised with time delay inputs can predict the time series with more accurately and effectively than the existing methods.  

A Chaotic Prediction Method to Time Series Data

2010 ◽  
Vol 113-116 ◽  
pp. 1367-1370 ◽  
Author(s):  
Bin Sheng Liu ◽  
Ying Wang ◽  
Xue Ping Hu
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Time Series ◽  
Drinking Water ◽  
Time Series Data ◽  
Prediction Method ◽  
Drinking Water Quality ◽  
Series Data ◽  
Gray Model ◽  
Forecast Method

There are many ways to predict drinking water quality such as neural network, gray model, ARIMA. But the prediction precise is need to improve. This paper proposes a new forecast method according the characteristic of drinking water quality and the evidence showed that the prediction is effectively. So it is able to being used in actual prediction.

A Hybrid Wind Speed Prediction Approach Based on Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks for Digital Twin

2020 ◽  
Author(s):  
Weifei Hu ◽  
Yihan He ◽  
Zhenyu Liu ◽  
Jianrong Tan ◽  
Ming Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Wind Speed ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Digital Twin ◽  
Mode Decomposition

Abstract Precise time series prediction serves as an important role in constructing a Digital Twin (DT). The various internal and external interferences result in highly non-linear and stochastic time series data sampled from real situations. Although artificial Neural Networks (ANNs) are often used to forecast time series for their strong self-learning and nonlinear fitting capabilities, it is a challenging and time-consuming task to obtain the optimal ANN architecture. This paper proposes a hybrid time series prediction model based on ensemble empirical mode decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components, each of which is composed of single-frequency and stationary signal, and a residual signal. The decomposed signals are used to train the BO-LSTM neural networks, in which the hyper-parameters of the LSTM neural networks are fine-tuned by the BO algorithm. The following time series data are predicted by summating all the predictions of the decomposed signals based on the trained neural networks. To evaluate the performance of the proposed hybrid method (EEMD-BO-LSTM), this paper conducts a case study of wind speed time series prediction and has a comprehensive comparison between the proposed method and other approaches including the persistence model, ARIMA, LSTM neural networks, B0-LSTM neural networks, and EEMD-LSTM neural networks. Results show an improved prediction accuracy using the EEMD-BO-LSTM method by multiple accuracy metrics.

Multi-dimensional Prediction Model for Cell Traffic in City Scale

2020 ◽  
pp. 2150010
Author(s):  
Hong Wang ◽  
Liqun Wang ◽  
Shufang Zhao ◽  
Xiuming Yue
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Base Stations ◽  
Series Data ◽  
Traffic Prediction ◽  
Percentage Error ◽  
Spatial Dimensions ◽  
Temporal And Spatial

Traffic prediction is a classical time series prediction which has been investigated in different domains, but most existing models are proposed based on limited time or spatial scale. Mobile cellular network traffic prediction is of paramount importance for quality-of-service (QoS) and power management of the cellular base stations, especially in the 5G era. Through the statistical analysis of the real historical traffic data obtained in a city scale spanning across multiple months, this paper makes an in-depth study of the temporal characteristics and behavior rules of the model data traffic. Considering that the time series data show different changing rules under the different time dimensions, spatial dimensions and independent dimensions, a multi-dimensional recurrent neural network (MDRNN) prediction model is established to predict the future cell traffic volume over various temporal and spatial dimensions. The data of this paper are trained and tested over real data of a city, and the granularity of the proposed prediction model can be drilled down to the cell level. Compared with the traditional trend fitting method, the proposed model achieves mean absolute percentage error (MAPE) reduction of 6.56%, and provides guidance for energy efficiency optimization and power consumption reduction of base stations in various temporal and spatial dimensions.

scholarly journals Time Series Prediction Based on Complex-Valued S-System Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Bin Yang ◽  
Wenzheng Bao ◽  
Yuehui Chen
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
System Model ◽  
Series Data ◽  
Classification Problems ◽  
Mathematical Formulas ◽  
Input Variables ◽  
Complex Valued

Symbolic regression has been utilized to infer mathematical formulas in order to solve the complex prediction and classification problems. In this paper, complex-valued S-system model (CVSS) is proposed to predict real-valued time series data. In a CVSS model, input variables and rate constants are complex-valued. The time series data need to be translated into complex numbers. The hybrid evolutionary algorithm based on complex-valued restricted additive tree and firefly algorithm is proposed to search the optimal CVSS model. Three financial time series data and Mackey–Glass chaos time series are collected to evaluate our proposed method. The experiment results show that the predicted data are very close to the target ones and our method could obtain the better RMSE, MAP, MAPE, POCID, R2, and ARV performances than ARIMA, radial basis function neural network (RBFNN), flexible neural tree (FNT), ordinary differential equation (ODE), and S-system.

scholarly journals FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 90 ◽  
Author(s):  
Ana Pano-Azucena ◽  
Esteban Tlelo-Cuautle ◽  
Sheldon Tan ◽  
Brisbane Ovilla-Martinez ◽  
Luis de la Fraga
Keyword(s):  
Time Series ◽  
Multilayer Perceptron ◽  
Time Series Data ◽  
Mean Squared Error ◽  
Time Series Prediction ◽  
Chaotic Time Series ◽  
Series Data ◽  
Support Vector ◽  
Maximum Lyapunov Exponent ◽  
Inference System

Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series.

Short-Term Time Series Prediction for a Logistics Outsourcing Company

2013 ◽  
pp. 150-160
Author(s):  
Angeliki Papana
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Linear Time ◽  
Time Series Prediction ◽  
Service Level ◽  
Optimal Choice ◽  
Series Data ◽  
Short Term ◽  
Univariate Time Series ◽  
Nonlinear Components

In this chapter, tools from univariate time series analysis and forecasting are presented and applied. Time series components, such as trend and seasonality are introduced and discussed, while time series methods are analyzed based on the type of the time series components. In the literature, linear methods are the most commonly used. However, real time series data often include nonlinear components, so linear time series forecasting may not be the optimal choice. Therefore, also a basic nonlinear forecasting method is presented. The necessity of these methods to logistics service providers and 3PL companies is presented by case studies that present how the operational and management costs can be cut down in order to ensure a service level. Short term forecasts are useful in all the units of activation of 3PL companies, i.e. supplies, production, distribution, storage, transportation, and service of customers.

Sign in / Sign up

Export Citation Format

Share Document