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

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.