In the field of financial prediction, most studies focus on individual stocks or stock indices. Stock sectors are collections of stocks with similar characteristics and the indices of sectors have more stable trends and predictability compared to individual stocks. Additionally, stock sectors are subsets of stock indices, which implies that investment portfolios based on stock sectors have a greater potential to achieve excess returns. In this paper, we propose a new method, Time-aware Graph Structure Learning Network (TGSLN), to address the problem of stock sector ranking recommendation. In this model, we use an indicator called Relative Price Strength (RPS) to describe the ranking change trend of the sectors. To construct the inherent connection between sectors, we construct a multi-variable time series that consists of multi-scale RPS sequences and effective indicators filtered through the factor selector. We also build a stock sector relation graph based on authoritative stock sector classifications. Specially, we design a time-aware graph structure learner, which can mine the sector relations from time series, and enhance the initial graph through graph fusion. Our model outperforms state-of-the-art baselines in both A-share and NASDAQ markets.

Multivariate time series (MTS) prediction has been widely applied in a diverse range of fields including electricity, economics, finance, and traffic. Many studies have successfully constructed spatial and temporal convolution modules called spatio-temporal block (ST-block) for multivariate time series prediction. However, existing methods need to manually design the architecture topology based on ST-blocks, which is time-consuming and requires extensive expert experience. In this paper, we propose a Spatio-Temporal based Architecture Topology Search (STATS) method for multivariate time series prediction, which can automatically design the ST-block for multivariate time series prediction. In the STATS, we construct static and dynamic graphs topologically to integrate both static and dynamic information to obtain more expressive ST-graphs for the prediction task. Then, STATS explores the architecture topology with the differentiable search algorithm based on ST-blocks automatically. Extensive experiments on four commonly used multivariate time series prediction benchmark datasets demonstrate that our proposed method STATS can outperform the state-of-the-art baseline models.

Multivariate time series (MTS) forecasting is a challenging task. In MTS forecasting, We need to consider both intra-series temporal correlations and inter-series spatial correlations simultaneously. However, existing methods capture spatial correlations from the local data of the time series, without taking the global historical information of time series into account. In addition, most methods base on graph neural network mining for the temporal correlations tend to the redundancy of information at adjacent time points in the time-series data, which introduces noise. In this paper, we propose a memory augmented graph learning network (MAGL), which captures the spatial correlations in terms of the global historical features of MTS. Specifically, we use a memory unit to learn from the local data of MTS. The memory unit records the global historical features of the time series, which is used to mine the spatial correlations. We also design a temporal feature distiller to reduce the noise in extracting temporal features. We extensively evaluate our model on four real-world datasets, comparing with several state-of-the-art methods. The experimental results show MAGL outperforms the state-of-the-art baseline methods on several datasets.