Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  it is introduced a new perturbative approach for time series forecasting. The model uses the error of the series, that is the difference beten real value of the series and the output of a predictive method, to improve the series forecasting. The methodology proposed is inspired in the Perturbation Theory, that consists in a set of approximation schemes used to describe a complicated problem in terms of simpler ones. For an experimental investigation, this theory, is combined with the TAEF method, that has interesting results when compared with the literature. This combination is called P-TAEF (Perturbative TAEF). Its results over some time series are discussed and compared with previous results found in the literature. It was used several performance measures that shod the robustness of the perturbative approach.
['Paulo S. G. de Mattos Neto', 'Aranildo Rodrigues Lima Junior', 'Tiago A. E. Ferreira', 'George D. C. Cavalcanti']
I am writing a research paper on a idea An intelligent perturbative approach for the time series forecasting problem
[{"from": "human", "value": "I am writing a research paper on a idea An intelligent perturbative approach for the time series forecasting problem"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  it is introduced a new perturbative approach for time series forecasting. The model uses the error of the series, that is the difference beten real value of the series and the output of a predictive method, to improve the series forecasting. The methodology proposed is inspired in the Perturbation Theory, that consists in a set of approximation schemes used to describe a complicated problem in terms of simpler ones. For an experimental investigation, this theory, is combined with the TAEF method, that has interesting results when compared with the literature. This combination is called P-TAEF (Perturbative TAEF). Its results over some time series are discussed and compared with previous results found in the literature. It was used several performance measures that shod the robustness of the perturbative approach."}]