当今, 人工智能已经广泛应用到各个领域中, 并取得了显著的效果. 数据归一化是人工智能应用
落地中的一个重要环节, 它有助于避免神经网络因数据量纲的复杂性对数据进行错误建模 …

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 …

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 …

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 …

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 …

The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes
over time, largely hinders the performance of TSF models. Existing works towards …

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) …

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 …

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 …

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 …