Parallel Stock Prediction using Python
SUMIT HARESH GANGARAMANI
Aug 04, 2021
Argument that real time stock prediction can be done effectively using parallel execution has been presented.(Using High-performance computing and combining the outputs of all the models. ).
Introduction
Large availability of stock market data but it’s use in forecasting stock prices is less.
Motivation arises from the above fact to apply model parallelism.
Argument that real time stock prediction can be done effectively using parallel execution has been presented.
This happens by combining the outputs of all the models.
Objectives
Model parallelism
Data exploration
Stacking Models
Multiprocessing
Methodology
Different Models used are LSTM, GRU, Vanilla, CNN-Seq2seq:
- LSTM
- Vanilla
- CNN-Seq2seq
Different libraries used are:
- Multiprocessing
- Tensorflow
- Keras
- Others
Project Files
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