Financial Time Series Data Prediction by Combination Model Adaboost-KNN-LSTM.

Financial time series is the main research direction of financial researchers due to its important economic value. However, owing to stochastic nature of financial markets, the data used by financial time series models need to have a series of features. This limits the use of financial forecasting models. A model for small datasets is proposed to address the above limitations. This model combines Adaptive Boosting(AdaBoost), KNearest Neighbors(KNN) and Long Short-Term Memory(LSTM) to accurately predict financial time series data by using a small number of features and data. Compared with classical machine learning models, the proposed model has the best prediction accuracy.