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.

翻译：模糊时间序列预测（FTSF）是一种应用广泛的典型预测方法。传统FTSF被视为专家系统，导致其无法识别未定义特征，这是FTSF预测效果不佳的主要原因。为解决该问题，所提出的差分模糊卷积神经网络（DFCNN）利用卷积神经网络重新实现具有可学习能力的FTSF。DFCNN能够识别潜在信息并提高预测精度。得益于神经网络的可学习能力，FTSF中建立的模糊规则长度可扩展至专家系统无法处理的任意长度。同时，由于非平稳时间序列的趋势性，FTSF通常难以获得满意性能。非平稳时间序列的趋势会导致FTSF建立的模糊集失效，进而造成预测失败。DFCNN利用差分算法削弱时间序列的非平稳性，从而能够以低误差预测FTSF无法达到满意性能的非平稳时间序列。经过大量实验验证，DFCNN具有卓越的预测效果，优于现有FTSF及常见时间序列预测算法。最后，DFCNN为改进FTSF提供了新思路，具有持续研究价值。