Financial time-series prediction has been an important topic in deep learning, and the prediction of financial time series is of great importance to investors, commercial banks and regulators. This paper proposes a model based on multiplexed attention mechanisms and linear transformers to predict financial time series. The linear transformer model has a faster model training efficiency and a long-time forecasting capability. Using a linear transformer reduces the original transformer’s complexity and preserves the decoder’s multiplexed attention mechanism. The results show that the proposed method can effectively improve the prediction accuracy of the model, increase the inference speed of the model and reduce the number of operations, which has new implications for the prediction of financial time series.

As a fundamental assumption in simultaneous localization and mapping, the static scenes hypothesis can be hardly fulfilled in applications of indoor/outdoor navigation or localization. Recent works about simultaneous localization and mapping in dynamic scenes commonly use heavy pixel-level segmentation net to distinguish dynamic objects, which brings enormous calculations and limits the real-time performance of the system. That restricts the application of simultaneous localization and mapping on the mobile terminal. In this article, we present a lightweight system for monocular simultaneous localization and mapping in dynamic scenes, which can run in real time on central processing unit (CPU) and generate a semantic probability map. The pixel-wise semantic segmentation net is replaced with a lightweight object detection net combined with three-dimensional segmentation based on motion clustering. And a framework integrated with an improved weighted-random sample consensus solver is proposed to jointly solve the camera pose and perform three-dimensional object segmentation, which enables high accuracy and efficiency. Besides, the prior information of the generated map and the object detection results is introduced for better estimation. The experiments on the public data set, and in the real-world demonstrate that our method obtains an outstanding improvement in both accuracy and speed compared to state-of-the-art methods.