Least Square Support Vector Machines as an Alternative Method in Seasonal Time Series Forecasting
نویسنده
چکیده
The least square support vector machines (LSSSVM) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the applications of LSSVM model in a seasonal time series forecasting has not been widely investigated. This study aims at developing a LSSVM model to forecast seasonal time series data. To assess the effectiveness of this model, the airline passenger series exhibits nonlinear behaviour and shows multiplicative seasonal behaviour was applied. In order to obtain the optimal model parameters of the LSSVM, a grid search algorithm and cross-validation method were employed. In this study, seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) models are employed for forecasting the same data sets. Empirical results indicate that the LSSVM yields well forecasting performances. Thus, the LSSVM model provides a promising alternative for seasonal time series forecasting.
منابع مشابه
Short Term Load Forecasting Using Empirical Mode Decomposition, Wavelet Transform and Support Vector Regression
The Short-term forecasting of electric load plays an important role in designing and operation of power systems. Due to the nature of the short-term electric load time series (nonlinear, non-constant, and non-seasonal), accurate prediction of the load is very challenging. In this article, a method for short-term daily and hourly load forecasting is proposed. In this method, in the first step, t...
متن کاملA hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan
This paper proposes a hybrid methodology that exploits the unique strength of the seasonal autoregressive integrated moving average (SARIMA) model and the support vector machines (SVM) model in forecasting seasonal time series. The seasonal time series data of Taiwan’s machinery industry production values were used to examine the forecasting accuracy of the proposed hybrid model. The forecastin...
متن کاملRevenue forecasting using a least-squares support vector regression model in a fuzzy environment
Revenue forecasting is difficult but essential for companies that want to create high-quality revenue budgets, especially in an uncertain economic environment with changing government policies. Under these conditions, the subjective judgment of decision makers is a crucial factor in making accurate forecasts. This investigation develops a fuzzy least-squares support vector regression model with...
متن کاملAir passenger forecasting by using a hybrid seasonal decomposition and least squares support vector regression approach
In this study, a hybrid approach based on seasonal decomposition (SD) and least squares support vector regression (LSSVR) model is proposed for air passenger forecasting. In the formulation of the proposed hybrid approach, the air passenger time series are first decomposed into three components: trend-cycle component, seasonal factor and irregular component. Then the LSSVR model is used to pred...
متن کاملA NEW APPROACH BASED ON OPTIMIZATION OF RATIO FOR SEASONAL FUZZY TIME SERIES
In recent years, many studies have been done on forecasting fuzzy time series. First-order fuzzy time series forecasting methods with first-order lagged variables and high-order fuzzy time series forecasting methods with consecutive lagged variables constitute the considerable part of these studies. However, these methods are not effective in forecasting fuzzy time series which contain seasonal...
متن کامل