Discovery and Prediction of Stock Index Pattern via Three-Stage Architecture of TICC, TPA-LSTM and Multivariate LSTM-FCNs

Abstract

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
LongShort-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.