scholarly journals Residuals-based deep least square support vector machine with redundancy test based model selection to predict time series

2019 ◽  
Vol 24 (6) ◽  
pp. 706-715 ◽  
Author(s):  
Yanhua Yu ◽  
Jie Li
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Model Selection ◽  
Least Square ◽  
Support Vector ◽  
Redundancy Test

Traffic Speed Time Series Short Term Forecasting Using Aggregated Model

2014 ◽  
Vol 587-589 ◽  
pp. 2057-2062
Author(s):  
Jian Gu ◽  
Shu Yan Chen
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Least Square ◽  
Support Vector ◽  
Short Term ◽  
Support Vector Machine Regression ◽  
Prediction Ability ◽  
Aggregated Model ◽  
Traffic Speed ◽  
Short Term Forecasting

This paper integrated superiority from time series model and least square support vector machine regression model with data aggregation for traffic speed short term forecasting. Based on the results of traffic data variations analysis, the practicability that speed data can be aggregated to several periods was confirmed, and aggregated model can be developed to forecast the speed with auto regression (AR) model and support vector machine regression (SVR). Then the speed data in case study were integrated to 4 periods at the location of Remote Traffic Microwave Sensors (RTMS) 2047 on 2ndRing Road Expressway in Beijing. Arguments with coefficients from AR models then act as the independent variables of LSSVR in aggregated model. Short term traffic speed was predicted by aggregated model, and the results indicated that taking advantages of time periods variation rule inside the aggregated model would help save the model running time cost under the premise of accuracy with better prediction ability than LSSVR in certain conditions.

Time Series Forecasting using Least Square Support Vector Machine for Canadian Lynx Data

2014 ◽  
Vol 70 (5) ◽  
Author(s):  
Shuhaida Ismail ◽  
Ani Shabri
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Support Vector Machine ◽  
Moving Average ◽  
Absolute Error ◽  
Research Area ◽  
Time Series Forecasting ◽  
Least Square ◽  
Support Vector ◽  
Promising Alternative

Time series analysis and forecasting is an active research area over the last few decades. There are various kinds of forecasting models have been developed and researchers have relied on statistical techniques to predict the future. This paper discusses the application of Least Square Support Vector Machine (LSSVM) models for Canadian Lynx forecasting. The objective of this paper is to examine the flexibility of LSSVM in time series forecasting by comparing it with other models in previous research such as Artificial Neural Networks (ANN), Auto-Regressive Integrated Moving Average (ARIMA), Feed-Forward Neural Networks (FNN), Self-Exciting Threshold Auto-Regression (SETAR), Zhang’s model, Aladang’s hybrid model and Support Vector Regression (SVR) model. The experiment results show that the LSSVM model outperforms the other models based on the criteria of Mean Absolute Error (MAE) and Mean Square Error (MSE). It also indicates that LSSVM provides a promising alternative technique in time series forecasting.

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