Application of online multitask learning based on least squares support vector regression in the financial market

COVID19-MLSF: A multi-task learning-based stock market forecasting framework during the COVID-19 pandemic

The sudden outbreak of COVID-19 has dramatically altered the state of the global economy, and the stock market has become more volatile and even fallen sharply as a result of its negative impact, heightening investors' apprehension regarding the correlation between unexpected events and stock market volatility. Additionally, internal and external characteristics coexist in the stock market. Existing research has struggled to extract more effective stock market features during the COVID-19 outbreak using a single time-series neural network model. This paper presents a framework for multitasking learning-based stock market forecasting (COVID-19-MLSF), which can extract the internal and external features of the stock market and their relationships effectively during COVID-19.The innovation comprises three components: designing a new market sentiment index (NMSI) and COVID-19 index to represent the external characteristics of the stock market during the COVID-19 pandemic. Besides, it introduces a multi-task learning framework to extract global and local features of the stock market. Moreover, a temporal convolutional neural network with a multi-scale attention mechanism is designed (MA-TCN) alongside a Multi-View Convolutional-Bidirectional Recurrent Neural Network with Temporal Attention (MVCNN-BiLSTM-Att), adjusting the model to account for the changing status of COVID-19 and its impact on the stock market. Experiments indicate that our model achieves superior performance both in terms of predicting the accuracy of the China CSI 300 Index during the COVID-19 period and in terms of sing market trading.

A joint network of non-linear graph attention and temporal attraction force for geo-sensory time series prediction

Geo-sensory time series, such as the air quality and water distribution, are collected from numerous sensors at different geospatial locations in the same time interval. Each sensor monitors multiple parameters and generates multivariate time series. These time series change over time and vary geographically; hence, geo-sensory time series contain multi-scale spatial-temporal correlations, namely inter-sensor spatial-temporal correlations and intra-sensor spatial-temporal correlations. To capture spatial-temporal correlations, although various deep learning models have been developed, few of the models focus on capturing both correlations. To solve this problem, we propose simultaneously capture the inter- and intra-sensor spatial-temporal correlations by designing a joint network of non-linear graph attention and temporal attraction force(J-NGT) consisting two graph attention mechanisms. The non-linear graph attention mechanism can characterize node affinities for adaptively selecting the relevant exogenous series and relevant sensor series. The temporal attraction force mechanism can weigh the effect of past values on current values to represent the temporal correlation. To prove the superiority and effectiveness of our model, we evaluate our model in three real-world datasets from different fields. Experimental results show that our model can achieve better prediction performance than eight state-of-the-art models, including statistical models, machine learning models, and deep learning models. Furthermore, we conducted experiments to capture inter- and intra-sensor spatial-temporal correlations. Experimental results indicate that our model significantly improves performance by capturing both inter- and intra-sensor spatial-temporal correlations. This fully shows that our model has a greater advantage in geo-sensory time series prediction.