Comparative Study on Performance of Time Series Fitting and Prediction of SF6 Decomposition Gas Under Over-heated Condition by GM(1,1) and OBGM(1,N) Grey System

Accurate prediction of gasoline price is important for the automobile makers to adjust designs and productions as well as marketing plans of their products. It is also necessary for government agencies to set effective inflation monitoring and environmental protection policies. To predict future levels of the gasoline price, due to difficulties of obtaining accurate estimates of influential external factors, data driven time-series forecasting models thus become more suitable given the convenience and practicability they are providing. In this paper, five popular time-series forecasting models, i.e., ARIMA-GARCH, exponential smoothing, grey system, neural network, and support vector machines models, are applied to predict gasoline prices in China. Comparing the performances of these models, it is noted that for this specific time series, a parsimonious ARIMA model performs the best in predicting the gasoline prices for a short time horizon, while for the medium length and long run the SVR and FNN models outperforms others respectively.  

Download Full-text

Approximate Dynamic Models for Chemical Processes: A Comparative Study of Neural Networks and Nonlinear Time Series Modeling Techniques

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)50970-2 ◽

1992 ◽

Vol 25 (5) ◽

pp. 63-68 ◽

Cited By ~ 1

Author(s):

A. Raich ◽

X. Wu ◽

A. Cinar

Keyword(s):

Neural Networks ◽

Time Series ◽

Comparative Study ◽

Dynamic Models ◽

Nonlinear Time Series ◽

Chemical Processes ◽

Time Series Modeling ◽

Modeling Techniques

Download Full-text

Time Series Adaptive Online Prediction Method Combined with Modified LS-SVR and AGO

Mathematical Problems in Engineering ◽

10.1155/2012/985930 ◽

2012 ◽

Vol 2012 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Guo Yangming ◽

Zhang Lu ◽

Cai Xiaobin ◽

Ran Congbao ◽

Zhai Zhengjun ◽

...

Keyword(s):

Time Series ◽

Complex Systems ◽

Incremental Learning ◽

Learning Algorithm ◽

Computing Time ◽

Grey System Theory ◽

Good Prediction ◽

Grey System ◽

Online Prediction ◽

Prediction Approach

Fault or health condition prediction of the complex systems has attracted more attention in recent years. The complex systems often show complex dynamic behavior and uncertainty, which makes it difficult to establish a precise physical model. Therefore, the time series of complex system is used to implement prediction in practice. Aiming at time series online prediction, we propose a new method to improve the prediction accuracy in this paper, which is based on the grey system theory and incremental learning algorithm. In this method, the accumulated generating operation (AGO) with the raw time series is taken to improve the data quality and regularity firstly; then the prediction is conducted by a modified LS-SVR model, which simplifies the calculation process with incremental learning; finally, the inverse accumulated generating operation (IAGO) is performed to get the prediction results. The results of the prediction experiments indicate preliminarily that the proposed scheme is an effective prediction approach for its good prediction precision and less computing time. The method will be useful in actual application.

Download Full-text

A Comparative Study of Hourly Wind Speed and Power Forecasting Using Deep Learning Networks, Weka Time Series, and ARIMA Algorithms for Smart Grid Integration

10.1109/epec52095.2021.9621652 ◽

2021 ◽

Author(s):

Abdussalam T. Mohamed ◽

Hamed H. Aly ◽

Timothy A. Little

Keyword(s):

Time Series ◽

Deep Learning ◽

Wind Speed ◽

Smart Grid ◽

Comparative Study ◽

Grid Integration ◽

Learning Networks ◽

Power Forecasting

Download Full-text