A Transfer Learning Method Based on Transformer and Its Application in Financial Time-Series Forecasting
Financial market forecasting is generally considered to be one of the most challenging tasks in data mining.Transformer model has recently been successful in improving the accuracy of Financial Time-Series Forecasting(FTSF).However,due to the complex information as implicit and the small amount of labeled data available,current benchmarks have poor generalization ability in this field.In order to alleviate the overfitting problem caused by the lack of clean data,a deep Transfer Learning framework named TADA-FTSF which combines with Transformer and adversarial domain adaptation is proposed for TSF tasks in the financial field,to improve the reliability and accuracy of the deep forecasting model.
国家科技期刊平台
NSTL
万方数据
