Stock Price Prediction
A forecasting system built with recurrent architectures — RNN, LSTM, BiLSTM and GRU — trained and compared on historical stock data.
- Year
- 2024
- Engagement
- Internal R&D build
- Focus
- AI/ML, Data Engineering
- Status
- Open source
01 — The problem
Sequence models are usually demonstrated one at a time, which tells you nothing about whether the architecture choice actually mattered for the series in front of you.
02 — Our solution
Train four recurrent architectures on the same data under the same conditions and compare them directly, so the comparison is about the model rather than the preprocessing.
How it works
The pipeline, step by step — from the first input to the final output.
- 01
Data preparation
Historical price data is cleaned and windowed into sequences.
- 02
Model training
RNN, LSTM, BiLSTM and GRU models are trained on identical inputs.
- 03
Comparison
Predictions are evaluated against held-out data across architectures.
What we built
The outcomes
01
A controlled comparison across four sequence architectures
02
A reusable windowing and evaluation setup
Challenges we solved
Financial series resist prediction
The honest result of this kind of work is usually about how much signal is not there — which is worth measuring rather than hiding.
Technology stack
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