Open AccessSelf CNN‐based time series stream forecasting
Author(s) -
Zeng Zhiping,
Xiao Haidong,
Zhang Xinpeng
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.2626
Subject(s) - series (stratigraphy) , computer science , convolutional neural network , time series , artificial intelligence , data stream , artificial neural network , machine learning , pattern recognition (psychology) , data mining , telecommunications , paleontology , biology
Self‐learning convolutional neural network (self‐CNN) for time series stream forecasting is proposed. First, the proposed self‐CNN model was trained using the different types of the time series data. With the lapse of the time series stream the self‐CNN model was self‐trained again and again, which was using the previously predicted correct data as the input. Finally, the model was used to forecast the new time series data. The performance evaluation using the self‐CNN method forecast and generate from the financial time series stream shows that the proposed self‐CNN method performs better than the traditional Bollinger bands method.