Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Relevance vector machine (RVM) is a Bayesian version of the support vector machine, which with a sparse model representation, has appeared as a porful tool for time series forecasting. RVM has demonstrated better performance over other methods such as neural networks or ARIMA-based models.  proposes a wavelet-based RVM model to forecast stock indices. The time series of explanatory variables are decomposed by the wavelet basis, and the extracted time scale features served as inputs of a RVM to perform the nonparametric regression and forecasting. Compared with the traditional GARCH model forecasts, the new method shows superior performance, and reduces the root-mean-squared forecasting errors by nearly one order.
['Shian-Chang Huang', 'Tung-Kuang Wu']
I am writing a research paper on a idea Wavelet-Based Relevance Vector Machines for Stock Index Forecasting
[{"from": "human", "value": "I am writing a research paper on a idea Wavelet-Based Relevance Vector Machines for Stock Index Forecasting"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Relevance vector machine (RVM) is a Bayesian version of the support vector machine, which with a sparse model representation, has appeared as a porful tool for time series forecasting. RVM has demonstrated better performance over other methods such as neural networks or ARIMA-based models.  proposes a wavelet-based RVM model to forecast stock indices. The time series of explanatory variables are decomposed by the wavelet basis, and the extracted time scale features served as inputs of a RVM to perform the nonparametric regression and forecasting. Compared with the traditional GARCH model forecasts, the new method shows superior performance, and reduces the root-mean-squared forecasting errors by nearly one order."}]