Learn to Forecast Time Series Data Using ML & InfluxDB

This tutorial teaches users how to forecast time series data using machine learning (ML) and InfluxDB. The project uses the Prophet library, a popular open-source ML library developed by Facebook research, to handle seasonality and trend changes in historical weather data from London, UK. The code fetches historical weather data from the Open-Meteo API, stores it in InfluxDB using the serverless version of InfluxDB 3, reads the data from InfluxDB, and uses Prophet to forecast the temperature for the next 30 days. The tutorial highlights the merits and drawbacks of using ML models versus traditional statistical techniques like ARIMA and introduces time series-specific LLMs as a new approach to forecasting. The project demonstrates batch processing, which is suitable for long-term forecasts like weather, but notes that real-time forecasting is needed for applications like stock prices.

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