Zero-Shot Time-Series Forecasting with QuestDB and Google's TimesFM

QuestDB, an open-source time-series database, is leveraged for high-demand workloads like trading and mission control due to its ultra-low latency and high ingestion throughput. This tutorial explores three methods to load data from QuestDB into Python for use with machine learning tools, focusing on data engineering patterns rather than the model itself. The methods include direct SQL queries using REST API or ConnectorX, exporting data to Parquet for reproducible training pipelines, and accessing Parquet partitions directly for a lakehouse-style approach. The tutorial uses Google's TimesFM, a foundation model for time-series forecasting, to predict cryptocurrency trading volume and volatility using 1-minute BTC-USDT bars, demonstrating how QuestDB's data can be seamlessly integrated into Python ML workflows. This approach emphasizes the importance of efficient data movement from databases to ML models, highlighting QuestDB's capabilities for modern analytics and forecasting tasks.

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