LSTM-ARIMA with Attention and multiplicative decomposition for sophisticated stock forecasting.
Project description
Neural Stock Prophet
neuralstockprophet combines several techniques and algorithms to enhance and evaluate the robustness, stability, and interoperability of the stock price prediction algorithm. Stock Price Prediction using a machine learning algorithm helps discover the future value of company stock and other financial assets traded on an exchange. Whereas, the existing methods relied highly on model setup and tuning, without considering the variation of data. Also, the machine learning model faces the problems of overfitting and performance limitations.
Combined techniques:
- LSTM model with attention mechanisms
- Multiplicative decomposition
- ARIMA model
Installation
pip install neuralstockprophet
Getting Started
License
This project is licensed under the MIT License - see the LICENSE file for details.
TODO
There are further improvements that can be made. Please have a look at the TODO.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file neuralstockprophet-0.0.1.tar.gz.
File metadata
- Download URL: neuralstockprophet-0.0.1.tar.gz
- Upload date:
- Size: 8.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.9.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
c8c164d2af06de6c497946b6b0166491c033c8d694ff713a37e753a89096ba2d
|
|
| MD5 |
71fd3d0637f772ac0771e5b1ab90fd3a
|
|
| BLAKE2b-256 |
656733eb65501a51d87f5735eee66eeeae8c3793fa45a64f286e3091fea2caa9
|
File details
Details for the file neuralstockprophet-0.0.1-py3-none-any.whl.
File metadata
- Download URL: neuralstockprophet-0.0.1-py3-none-any.whl
- Upload date:
- Size: 10.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.9.6
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
e9096c3036dfa9ab284339721aedbcf14276f3ca51b363872ce87b8f32f3a0fe
|
|
| MD5 |
0e78027f8ba93adee118a7a0748dfe95
|
|
| BLAKE2b-256 |
a3dd135bc3fdb5f9df09e44d01e63b931f26ade28fca1587b6c8145cbbd56359
|
