forecasting stock-trade data using timescale and large time models

how to integrate large time models into timescale database

this project demonstrates how to integrate forecasts into a Timescale database, using TimeGPT.

set up env

pip install uv
uv venv --python 3.10
source .venv/bin/activate
uv pip install -e .
pre-commit install

set up timescale

Tip

when you create a timescale service, you can download the psql config file. also, you will be provided with a psql command such ass psql -d <your project id>. that's your project id.

finance tutorial part 1: loading and forecasting data on demand

this process follows the timescale documentation

1. create a service on timescale
2. save your credentials in the psql config file ~/.pg_service.conf
3. download the data and populate timescale using

make populate_timescale project_id=<your project id>
make create_materialized_view project_id=<your project id>
make create_forecasts_table project_id=<your project id>

1. forecast!

python forecast --h <your horizon>

finance tutorial part 2: loading and forecasting real time data

this process follows the timescale documentation

1. create a service on timescale
2. save your credentials in the psql config file ~/.pg_service.conf

make create_twelvedata_table project_id=<your project id>
make create_m_view_twelvedata project_id=<your project id>
make create_f_table_twelvedata project_id=<your project id>

1. collect real time data, ingest it to timescale and forecast it in real time!

python forecast/real_time.py