Search Results for author: Weiqing Liu

Found 13 papers, 6 papers with code

Instance-wise Graph-based Framework for Multivariate Time Series Forecasting

1 code implementation14 Sep 2021 Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

In this paper, we propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps for multivariate time series forecasting.

Multivariate Time Series Forecasting Time Series

Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation

no code implementations12 Jul 2021 Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian

Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance.

Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport

1 code implementation24 Jun 2021 Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian

In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns.

Stock Prediction

REST: Relational Event-driven Stock Trend Forecasting

no code implementations15 Feb 2021 Wentao Xu, Weiqing Liu, Chang Xu, Jiang Bian, Jian Yin, Tie-Yan Liu

To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts.

Universal Trading for Order Execution with Oracle Policy Distillation

no code implementations28 Jan 2021 Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, Tie-Yan Liu

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.

Algorithmic Trading

Learning to Use Future Information in Simultaneous Translation

1 code implementation1 Jan 2021 Xueqing Wu, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Tao Qin, Tie-Yan Liu

For wait-k inference, we observe that wait-m training with $m>k$ in simultaneous NMT (i. e., using more future information for training than inference) generally outperforms wait-k training.

Machine Translation

ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting

no code implementations11 Dec 2020 Hongshun Tang, Lijun Wu, Weiqing Liu, Jiang Bian

Stock trend forecasting has become a popular research direction that attracts widespread attention in the financial field.

Qlib: An AI-oriented Quantitative Investment Platform

1 code implementation22 Sep 2020 Xiao Yang, Weiqing Liu, Dong Zhou, Jiang Bian, Tie-Yan Liu

Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.

Portfolio Optimization Stock Market Prediction

Temporally Correlated Task Scheduling for Sequence Learning

1 code implementation10 Jul 2020 Xueqing Wu, Lewen Wang, Yingce Xia, Weiqing Liu, Lijun Wu, Shufang Xie, Tao Qin, Tie-Yan Liu

In many applications, a sequence learning task is usually associated with multiple temporally correlated auxiliary tasks, which are different in terms of how much input information to use or which future step to predict.

Machine Translation

Learning to Reweight with Deep Interactions

no code implementations9 Jul 2020 Yang Fan, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Jiang Bian, Tao Qin, Xiang-Yang Li

Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc.

Image Classification Machine Translation

Measuring Model Complexity of Neural Networks with Curve Activation Functions

no code implementations16 Jun 2020 Xia Hu, Weiqing Liu, Jiang Bian, Jian Pei

Our results demonstrate that the occurrence of overfitting is positively correlated with the increase of model complexity during training.

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