harveybc / predictor

Predictor that uses a configurable plugin to make predictions for a configurable time horizon in a timeseries, using heterogeneus data as input, assumming that the input data is synchronized with the timeseries to be predicted.

Predictor

Description

The predictor project is a comprehensive tool for timeseries prediction, equipped with a robust plugin architecture. This project allows for both local and remote configuration handling, as well as replicability of experimental results. The system can be extended with custom plugins for various types of neural networks, including artificial neural networks (ANN), convolutional neural networks (CNN), long short-term memory networks (LSTM), and transformer-based models.

Installation Instructions

To install and set up the predictor application, follow these steps:

1. Clone the Repository:

   git clone https://github.com/harveybc/predictor.git
   cd predictor

2. Create and Activate a Virtual Environment (Anaconda is required):

   • Using conda:

     conda create --name predictor-env python=3.9
     conda activate predictor-env

3. Install Dependencies:

   pip install --upgrade pip
   pip install -r requirements.txt

4. Build the Package:

   python -m build

5. Install the Package:

   pip install .

6. (Optional) Run the predictor:

   • On Windows, run the following command to verify installation (it uses all default valuex, use predictor.bat --help for complete command line arguments description):

     predictor.bat tests\data\EURUSD_hour_2010_2020.csv

   • On Linux, run:

     sh predictor.sh tests\data\EURUSD_hour_2010_2020.csv

7. (Optional) Run Tests: For pasing remote tests, requires an instance of harveybc/data-logger

   • On Windows, run the following command to run the tests:

     set_env.bat
     pytest

   • On Linux, run:

     sh ./set_env.sh
     pytest

8. (Optional) Generate Documentation:

   • Run the following command to generate code documentation in HTML format in the docs directory:

     pdoc --html -o docs app

9. (Optional) Install Nvidia CUDA GPU support:

Please read: Readme - CUDA

Usage

The application supports several command line arguments to control its behavior:

usage: predictor.bat tests\data\csv_sel_unb_norm_512.csv

Directory Structure

• app/: The main application package.

  • cli.py: Handles command-line argument parsing.
  • config.py: Stores default configuration values.
  • config_handler.py: Manages the loading, saving, and merging of configurations.
  • config_merger.py: Merges configurations from various sources.
  • data_handler.py: Handles the loading and saving of data.
  • data_processor.py: Processes input data and runs the prediction pipeline.
  • main.py: Main entry point for the application.
  • plugin_loader.py: Dynamically loads prediction plugins.
  • plugins/: Directory for prediction plugins.
    • predictor_plugin_ann.py: Predictor plugin using an artificial neural network.
    • predictor_plugin_cnn.py: Predictor plugin using a convolutional neural network.
    • predictor_plugin_lstm.py: Predictor plugin using long short-term memory networks.
    • predictor_plugin_transformer.py: Predictor plugin using transformer layers.

• tests/: Test modules for the application.

  • acceptance: User acceptance tests.
  • system: System tests.
  • integration: Integration tests.
  • unit: Unit tests.

• README.md: Overview and documentation for the project.

• requirements.txt: Lists Python package dependencies.

• setup.py: Script for packaging and installing the project.

• set_env.bat: Batch script for environment setup.

• set_env.sh: Shell script for environment setup.

• .gitignore: Specifies intentionally untracked files to ignore.