scholarly journals Image-Based Learning Approach Applied to Time Series Forecasting

2012 ◽  
Vol 10 (3) ◽  
Author(s):  
K. Ramírez-Amáro ◽  
J. C. Chimal-Eguía
Keyword(s):  
Time Series ◽  
Input Data ◽  
Data Representation ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Learning Approach ◽  
New Information ◽  
New Learning ◽  
Learning Technique ◽  
The Difference

In this paper, a new learning approach based on time-series image information is presented. In order to implement this new learning technique, a novel time-series input data representation is also defined. This input data representation is based on information obtained by image axis division into boxes. The difference between this new input data representation and the classical is that this technique is not time-dependent. This new information is implemented in the new Image-Based Learning Approach (IBLA) and by means of a probabilistic mechanism this learning technique is applied to the interesting problem of time series forecasting. The experimental results indicate that by using the methodology proposed in this article, it is possible to obtain better results than with the classical techniques such as artificial neuronal networks and support vector machines.

scholarly journals Time-Series Forecasting Models for Gasoline Prices in China

2018 ◽  
Vol 10 (12) ◽  
pp. 43
Author(s):  
Feng Xu ◽  
Mohamad Sepehri ◽  
Jian Hua ◽  
Sergey Ivanov ◽  
Julius N. Anyu
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Gasoline Prices ◽  
Support Vector ◽  
Grey System ◽  
Gasoline Price ◽  
Forecasting Models ◽  
Long Run ◽  
Environmental Protection Policies

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.  

Forecasting Demand With Support Vector Regression Technique Incorporating Feature Selection in the Presence of Calendar Effect

2018 ◽  
pp. 302-316 ◽  
Author(s):  
Malek Sarhani ◽  
Abdellatif El Afia
Keyword(s):  
Time Series ◽  
Feature Selection ◽  
Supply Chains ◽  
Support Vector Regression ◽  
Case Studies ◽  
Time Series Forecasting ◽  
Regression Technique ◽  
Support Vector ◽  
Reliable Prediction ◽  
Forecasting Methods

Reliable prediction of future demand is needed to better manage and optimize supply chains. However, a difficulty of forecasting demand arises due to the fact that heterogeneous factors may affect it. Analyzing such data by using classical time series forecasting methods will fail to capture such dependency of factors. This chapter addresses these problems by examining the use of feature selection in forecasting using support vector regression while eliminating the calendar effect using X13-ARIMA-SEATS. The approach is investigated in three different case studies.

scholarly journals Financial time series forecasting using twin support vector regression

PLoS ONE ◽  
2019 ◽  
Vol 14 (3) ◽  
pp. e0211402 ◽  
Author(s):  
Deepak Gupta ◽  
Mahardhika Pratama ◽  
Zhenyuan Ma ◽  
Jun Li ◽  
Mukesh Prasad
Keyword(s):  
Time Series ◽  
Support Vector Regression ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Support Vector ◽  
Twin Support Vector Regression ◽  
Financial Time
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