Trend-Based Time Series Prediction Algorithm

According to the resources performance and status information provided by grid monitoring system, this paper adopts a trend-based time series prediction algorithm to predict short-term performance of the resources. Experiments show that the improved mixed trend-based prediction algorithm tracks the trend of data changes by giving more weight, simultaneously takes the different situations of data increases and decreases into account, so the improved algorithm is superior to the pre-improved and it improves the accuracy of the prediction effectively.

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Compound Autoregressive Network for Prediction of Multivariate Time Series

Complexity ◽

10.1155/2019/9107167 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 13

Author(s):

Yuting Bai ◽

Xuebo Jin ◽

Xiaoyi Wang ◽

Tingli Su ◽

Jianlei Kong ◽

...

Keyword(s):

Time Series ◽

Time Series Prediction ◽

Multivariate Time Series ◽

Prediction Algorithm ◽

Short Term ◽

Advanced Control ◽

Multiple Variables ◽

Nonlinear Autoregressive Model ◽

Interactive Relation ◽

Nonlinear Autoregressive

The prediction information has effects on the emergency prevention and advanced control in various complex systems. There are obvious nonlinear, nonstationary, and complicated characteristics in the time series. Moreover, multiple variables in the time-series impact on each other to make the prediction more difficult. Then, a solution of time-series prediction for the multivariate was explored in this paper. Firstly, a compound neural network framework was designed with the primary and auxiliary networks. The framework attempted to extract the change features of the time series as well as the interactive relation of multiple related variables. Secondly, the structures of the primary and auxiliary networks were studied based on the nonlinear autoregressive model. The learning method was also introduced to obtain the available models. Thirdly, the prediction algorithm was concluded for the time series with multiple variables. Finally, the experiments on environment-monitoring data were conducted to verify the methods. The results prove that the proposed method can obtain the accurate prediction value in the short term.

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2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series

Energies ◽

10.3390/en14092392 ◽

2021 ◽

Vol 14 (9) ◽

pp. 2392

Author(s):

Antonello Rosato ◽

Rodolfo Araneo ◽

Amedeo Andreotti ◽

Federico Succetti ◽

Massimo Panella

Keyword(s):

Time Series ◽

Short Term Memory ◽

Time Series Prediction ◽

Short Term ◽

Neural Architecture ◽

Network Layers ◽

Learning Scheme ◽

World Energy ◽

Multivariate Prediction ◽

Long Short Term Memory

Here, we propose a new deep learning scheme to solve the energy time series prediction problem. The model implementation is based on the use of Long Short-Term Memory networks and Convolutional Neural Networks. These techniques are combined in such a fashion that inter-dependencies among several different time series can be exploited and used for forecasting purposes by filtering and joining their samples. The resulting learning scheme can be summarized as a superposition of network layers, resulting in a stacked deep neural architecture. We proved the accuracy and robustness of the proposed approach by testing it on real-world energy problems.

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