A Novel Modeling Method Based on Multi-Dimensional Taylor Network and its Application in Time Series Prediction

2014 ◽  
Vol 940 ◽  
pp. 480-484 ◽  
Author(s):  
Yi Lin ◽  
Hong Sen Yan ◽  
Bo Zhou
Keyword(s):  
Time Series ◽  
Prior Knowledge ◽  
Dynamic Characteristics ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Nonlinear Time Series ◽  
Modeling Method ◽  
Input Output ◽  
Output Data ◽  
Bridge Tower

A novel modeling method based on multi-dimensional Taylor network is proposed. The structure and the principle of the multi-dimensional Taylor network are introduced. Based on this, the method is applied in the nonlinear time series prediction based on multi-dimensional Taylor network. It provides a new method to predict the time series, which can describe the dynamic characteristics without prior knowledge and can realize the prediction of the nonlinear time series just with input-output data. An example of predicting the stress data of a large span bridge tower induced by strong typhoon is taken at last in this paper. Results indicate the validity and the better prediction accuracy of this method in nonlinear time series prediction.

A Novel Nonlinear Time Series Prediction Method and its Application in Structural Vibration Response Prediction

2014 ◽  
Vol 599-601 ◽  
pp. 1918-1921 ◽  
Author(s):  
Yi Lin ◽  
Hong Sen Yan ◽  
Bo Zhou
Keyword(s):  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Response Prediction ◽  
Nonlinear Time Series ◽  
Vibration Response ◽  
Structural Vibration ◽  
Input Output ◽  
Output Data

A novel nonlinear time series prediction method is proposed in this paper. This prediction method is based on the Multi-dimensional Taylor Network. The structure of the Multi-dimensional Taylor Network is introduced firstly. The Multi-dimensional Taylor Network provides a new method to predict the nonlinear time series. The prediction model based on the Multi-dimensional Taylor Network can realize the prediction of the nonlinear time series just with input-output data without the system mechanism, and it can describe the dynamic characteristics of the system. Finally, the new prediction method is applied in the structural vibration response prediction. Results indicate the validity and the better prediction accuracy of this method.

scholarly journals Fuzzy Transfer Learning in Time Series Forecasting for Stock Market Prices

2021 ◽  
Author(s):  
Shanoli Samui Pal ◽  
Samarjit Kar
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Prior Knowledge ◽  
Transfer Learning ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Series Data ◽  
Data Sets ◽  
Market Data

Abstract Transfer learning involves transferring prior knowledge of solving similar problems in order to achieve quick and efficient solution. The aim of fuzzy transfer learning is to transfer prior knowledge in an imprecise environment. Time series like stock market data are non-linear in nature and movement of stock is uncertain, so it is quite difficult following the stock market and in decision making. In this study, we propose a method to forecast stock market time series in the situation when we can use prior experience to make decisions. Fuzzy transfer learning (FuzzyTL) is based on knowledge transfer in that and adapting rules obtained domain. Three different stock market time series data sets are used for comparative study. It is observed that the effect of knowledge transferring works well together with smoothing of dependent attributes as the stock market data fluctuate with time. Finally, we give an empirical application in Shenzhen stock market with larger data sets to demonstrate the performance of the model. We have explored FuzzyTL in time series prediction to unerstand the essence of FuzzyTL. We were working on the question of the capability of FuzzyTL in improving prediction accuracy. From the comparisons, it can be said fuzzy transfer learning with smoothing improves prediction accuracy efficiently.

scholarly journals Nonlinear time series prediction algorithm based on AD-SSNET for artificial intelligence–powered Internet of Things

2021 ◽  
Vol 17 (3) ◽  
pp. 155014772110041
Author(s):  
Banteng Liu ◽  
Wei Chen ◽  
Meng Han ◽  
Zhangquan Wang ◽  
Ping Sun ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Internet Of Things ◽  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Time Series Prediction ◽  
Nonlinear Time Series ◽  
Prediction Algorithm ◽  
Subtractive Clustering ◽  
Wireless Internet

Time series have broad usage in the wireless Internet of Things. This article proposes a nonlinear time series prediction algorithm based on the Small-World Scale-Free Network after the AIC-Optimized Subtractive Clustering Algorithm (AIC-DSCA-SSNET, AD-SSNET) to predict the nonlinear and unstable time series, which improves the prediction accuracy. The AD-SSNET is introduced as a reservoir based on the echo state network to improve the predictive capability of nonlinear time series, and combined with artificial intelligence method to construct the prediction model training samples. First, the optimal clustering scheme of randomly distributed neurons in the network is adaptively obtained by the AIC-DSCA, then the AD-SSNET is constructed according to the intra-cluster priority connection algorithm. Finally, the reservoir synaptic matrix is calculated according to the synaptic information. Experimental results show that the proposed nonlinear time series prediction algorithm extends the feasible range of spectral radii of the reservoir, improves the prediction accuracy of nonlinear time series, and has great significance to time series analysis in the era of wireless Internet of Things.

Nonlinear Time-Series Prediction with Missing and Noisy Data

1998 ◽  
Vol 10 (3) ◽  
pp. 731-747 ◽  
Author(s):  
Volker Tresp ◽  
Reimar Hofmann
Keyword(s):  
Time Series ◽  
Stochastic Simulation ◽  
Time Series Prediction ◽  
Noisy Data ◽  
Nonlinear Time Series ◽  
Point Of View ◽  
Data Set ◽  
Sunspot Data ◽  
Error Bars ◽  
Probabilistic Point

We derive solutions for the problem of missing and noisy data in nonlinear time-series prediction from a probabilistic point of view. We discuss different approximations to the solutions—in particular, approximations that require either stochastic simulation or the substitution of a single estimate for the missing data. We show experimentally that commonly used heuristics can lead to suboptimal solutions. We show how error bars for the predictions can be derived and how our results can be applied to K-step prediction. We verify our solutions using two chaotic time series and the sunspot data set. In particular, we show that for K-step prediction, stochastic simulation is superior to simply iterating the predictor.

scholarly journals A Dual-Attention Recurrent Neural Network Method for Deep Cone Thickener Underflow Concentration Prediction

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1260
Author(s):  
Zhaolin Yuan ◽  
Jinlong Hu ◽  
Di Wu ◽  
Xiaojuan Ban
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Experimental Result ◽  
Common Error ◽  
Neural Network Method ◽  
Multiple Sensors ◽  
Network Method ◽  
Deep Cone Thickener

This paper focuses on the time series prediction problem for underflow concentration of deep cone thickener. It is commonly used in the industrial sedimentation process. In this paper, we introduce a dual attention neural network method to model both spatial and temporal features of the data collected from multiple sensors in the thickener to predict underflow concentration. The concentration is the key factor for future mining process. This model includes encoder and decoder. Their function is to capture spatial and temporal importance separately from input data, and output more accurate prediction. We also consider the domain knowledge in modeling process. Several supplementary constructed features are examined to enhance the final prediction accuracy in addition to the raw data from sensors. To test the feasibility and efficiency of this method, we select an industrial case based on Industrial Internet of Things (IIoT). This Tailings Thickener is from FLSmidth with multiple sensors. The comparative results support this method has favorable prediction accuracy, which is more than 10% lower than other time series prediction models in some common error indices. We also try to interpret our method with additional ablation experiments for different features and attention mechanisms. By employing mean absolute error index to evaluate the models, experimental result reports that enhanced features and dual-attention modules reduce error of fitting ~5% and ~11%, respectively.

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