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

通过使用微分模糊卷积神经网络，本文提出的新模型具有学习能力和对非静态时间序列的预测准确性，其预测效果优于现有的模糊时间序列预测方法以及普通的时间序列预测算法，可进一步提高模糊时间序列预测的准确率。

差分卷积模糊时间序列预测

Differential Convolutional Fuzzy Time Series Forecasting

This study presents a novel constraint-based causal discovery approach for
autocorrelated and non-stationary time series data (CDANs). Our proposed method
addresses several limitations of existing causal discovery methods for
autocorrelated and non-stationary time series data, such as high
dimensionality, the inability to identify lagged causal relationships, and the
overlook of changing modules. Our approach identifies both lagged and
instantaneous/contemporaneous causal relationships along with changing modules
that vary over time. The method optimizes the conditioning sets in a
constraint-based search by considering lagged parents instead of conditioning
on the entire past that addresses high dimensionality. The changing modules are
detected by considering both contemporaneous and lagged parents. The approach
first detects the lagged adjacencies, then identifies the changing modules and
contemporaneous adjacencies, and finally determines the causal direction. We
extensively evaluated the proposed method using synthetic datasets and a
real-world clinical dataset and compared its performance with several baseline
approaches. The results demonstrate the effectiveness of the proposed method in
detecting causal relationships and changing modules in autocorrelated and
non-stationary time series data.

该研究提出了一种新颖的基于约束条件的因果发现方法，用于自相关和非平稳时间序列数据中的模块变化检测和因果关系识别。

CDANs: 自自相关和非平稳时间序列数据的时间因果发现

CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data

The paper describes the deep learning approach for forecasting non-stationary
time series with using time trend correction in a neural network model. Along
with the layers for predicting sales values, the neural network model includes
a subnetwork block for the prediction weight for a time trend term which is
added to a predicted sales value. The time trend term is considered as a
product of the predicted weight value and normalized time value. The results
show that the forecasting accuracy can be essentially improved for
non-stationary sales with time trends using the trend correction block in the
deep learning model.

该论文描述了一种深度学习方法，利用时间趋势修正在神经网络模型中预测非平稳时间序列。结果表明，在深度学习模型中加入时间趋势修正块可以显著提高非平稳销售的预测准确性。