Non-stationary transformers: Exploring the stationarity in time series forecasting
Transformers have shown great power in time series forecasting due to their global-range
modeling ability. However, their performance can degenerate terribly on non-stationary real …
modeling ability. However, their performance can degenerate terribly on non-stationary real …
Nonstationary time series transformation methods: An experimental review
Data preprocessing is a crucial step for mining and learning from data, and one of its primary
activities is the transformation of data. This activity is very important in the context of time …
activities is the transformation of data. This activity is very important in the context of time …
Machine learning-based IoT-botnet attack detection with sequential architecture
With the rapid development and popularization of Internet of Things (IoT) devices, an
increasing number of cyber-attacks are targeting such devices. It was said that most of the …
increasing number of cyber-attacks are targeting such devices. It was said that most of the …
Contrastive learning of subject-invariant EEG representations for cross-subject emotion recognition
EEG signals have been reported to be informative and reliable for emotion recognition in
recent years. However, the inter-subject variability of emotion-related EEG signals still poses …
recent years. However, the inter-subject variability of emotion-related EEG signals still poses …
Dish-ts: a general paradigm for alleviating distribution shift in time series forecasting
The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes
over time, largely hinders the performance of TSF models. Existing works towards …
over time, largely hinders the performance of TSF models. Existing works towards …
A real-time adaptive model for bearing fault classification and remaining useful life estimation using deep neural network
Rolling element bearings are essential components of a wide variety of industrial machinery
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
and the leading cause of equipment failure. The prediction of Remaining Useful Life (RUL) …
Deep adaptive input normalization for time series forecasting
Deep learning (DL) models can be used to tackle time series analysis tasks with great
success. However, the performance of DL models can degenerate rapidly if the data are not …
success. However, the performance of DL models can degenerate rapidly if the data are not …
Impact of data normalization on stock index forecasting
Forecasting the behavior of the financial market is a nontrivial task that relies on the
discovery of strong empirical regularities in observations of the system. These regularities …
discovery of strong empirical regularities in observations of the system. These regularities …
Mixture of activation functions with extended min-max normalization for forex market prediction
An accurate exchange rate forecasting and its decision-making to buy or sell are critical
issues in the Forex market. Short-term currency rate forecasting is a challenging task due to …
issues in the Forex market. Short-term currency rate forecasting is a challenging task due to …
Estimate soil moisture of maize by combining support vector machine and chaotic whale optimization algorithm
B He, B Jia, Y Zhao, X Wang, M Wei… - Agricultural Water …, 2022 - Elsevier
Soil moisture of maize has an extremely important impact on the growth and development of
maize. Failure to accurately estimate soil moisture will lead to severe reductions in maize …
maize. Failure to accurately estimate soil moisture will lead to severe reductions in maize …