Prediction of Stock Index Pattern via three-stage architecture of TICC, TPA-LSTM and Multivariate LSTM-FCNs

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Amjan Shaik, Nitin Bisht, Vijay Kumar, Nihar Raghvendra, Rohit Gupta


In this study, we attempt to identify and forecast stock indicator patterns using the analysis of multivariate time series. Our argument is based on the idea that pattern exploration and supply signal price forecasting may be more logical and efficient than more established techniques like the Autoregressive Integrated Moving Ordinary (ARIMA) version in financial planning. Toeplitz Inverse Covariance-Based Clustering (TICC), Temporal Pattern Attention and Lengthy Short-Term Memory (TPA- LSTM), as well as Multivariate LSTM-FCNs (MLSTM- FCN and MALSTM- FCN) are used to create a three-phase armature for pattern recognition and supply signal vetting. Initially, we use TICC to look for duplicate stock signal patterns. In the alternative phase, TPA- LSTM is used to prognosticate multivariate supply indicators by taking into account weak regular patterns as well as extended short-term data.The predictive supply indicator cost pattern and MALSTM- FCN are associated at some point in the third phase. Eleven synthetic sub-indices and the Hangseng Supply Index are both used in the test. The 3-phase armature achieves adequate and also better efficiency than common styles, such as Ignorant Bayes Classifier(NB), Assistance Vector Device Classifier(SVM), Random Forest(RF), and so on, according to empirical data. Also, in order to further investigate the effectiveness of the suggested three step armature, we construct equal percentage profiles based on bullish trading regulations. In the exam, 7 complete stock signals are used. According to empirical findings, the portfolio based on the suggested three-stage armature offers much higher efficiency than the demand-based portfolio. These results might provide a completely new route for risk aversion and profile construction.

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