In this study, we attempt to predict global agricultural commodity futures prices through analysis of multivariate time series. Our motivation is based on the notion that datasets of agricultural commodity futures prices involves a mixture of long-and short-term information, linear and non-linear structure, for which traditional approaches such as Auto-Regressive Integrated Moving Average (ARIMA) and Vector Auto-Regression (VAR) may fail. To tackle this issue, Long-and Short-Term Time-series Network (LSTNet) is applied for prediction. Empirical results show that LSTNet achieves better performance over that of several state-of-the-art baseline methods on average and in most periods based on three performance evaluation measures and two tests of performance difference.

In this study, we attempt to discover and predict stock index patterns through analysis of multivariate time series. Our motivation is based on the notion that financial planning guided by pattern discovery and prediction of stock index prices maybe more realistic and effective than traditional approaches, such as Autoregressive Integrated Moving Average (ARIMA) model. A three-stage architecture constructed by combining Toeplitz Inverse Covariance-Based Clustering (TICC), Temporal Pattern Attention and Long-Short-Term Memory (TPA-LSTM) and Multivariate LSTM-FCNs (MLSTM-FCN and MALSTM-FCN) is applied for pattern discovery and prediction of stock index. In the first stage, we use TICC to discover repeated patterns of stock index. Then, in the second stage, TPA-LSTM that considers weak periodic patterns and long short-term information is used to predict multivariate stock indices. Finally, in the third stage, MALSTM-FCN is applied to predict stock index price pattern. The Hangseng Stock Index and eleven industrial sub-indices are used in the experiment. Empirical results show that the three-stage architecture achieves satisfactory and better performance than traditional methods, such as Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), Random Forest (RF), etc. Moreover, we construct equal proportion portfolios based on the bullish trading rules to further analyze the feasibility of the proposed threestage architecture. Seven comprehensive stock indices are used in the experiment. Empirical results show that the portfolio based on the proposed three-stage architecture presents better performance than the marketbased portfolio. These findings may provide new direction for the portfolio construction and risk aversion. INDEX TERMS Stock index pattern, pattern discovery, pattern prediction, multivariate time series.

The existing spatial panel structural vector auto-regressive model can effectively capture the time and spatial dynamic dependence of endogenous variables. However, the hypothesis that the common factors have the same effect for all spatial units is unreasonable. Therefore, incorporating time effects, spatial effects, and time-individual effects, this paper develops a more general spatial panel structural vector autoregressive model with interactive effects (ISpSVAR) that can reflect the different effects of common factors on different spatial units. Additionally, based on whether or not the common factors can be observed, this paper proposes procedures to estimate ISpSVAR separately and studies the finite sample properties of estimators by Monte Carlo simulation. The simulation results show the effectiveness of the proposed ISpSVAR model and its estimation procedures.

This study attempts to predict stock index prices using multivariate time series analysis. The study’s motivation is based on the notion that datasets of stock index prices involve weak periodic patterns, long-term and short-term information, for which traditional approaches and current neural networks such as Autoregressive models and Support Vector Machine (SVM) may fail. This study applied Temporal Pattern Attention and Long-Short-Term Memory (TPA-LSTM) for prediction to overcome the issue. The results show that stock index prices prediction through the TPA-LSTM algorithm could achieve better prediction performance over traditional deep neural networks, such as recurrent neural network (RNN), convolutional neural network (CNN), and long and short-term time series network (LSTNet).