  Fuzzy time series forecasting (FTSF) is a typical forecasting method with
wide application. Traditional FTSF is regarded as an expert system which leads
to loss of the ability to recognize undefined features. The mentioned is the
main reason for poor forecasting with FTSF. To solve the problem, the proposed
model Differential Fuzzy Convolutional Neural Network (DFCNN) utilizes a
convolution neural network to re-implement FTSF with learnable ability. DFCNN
is capable of recognizing potential information and improving forecasting
accuracy. Thanks to the learnable ability of the neural network, the length of
fuzzy rules established in FTSF is expended to an arbitrary length that the
expert is not able to handle by the expert system. At the same time, FTSF
usually cannot achieve satisfactory performance of non-stationary time series
due to the trend of non-stationary time series. The trend of non-stationary
time series causes the fuzzy set established by FTSF to be invalid and causes
the forecasting to fail. DFCNN utilizes the Difference algorithm to weaken the
non-stationary of time series so that DFCNN can forecast the non-stationary
time series with a low error that FTSF cannot forecast in satisfactory
performance. After the mass of experiments, DFCNN has an excellent prediction
effect, which is ahead of the existing FTSF and common time series forecasting
algorithms. Finally, DFCNN provides further ideas for improving FTSF and holds
continued research value.
