Predicting Weather Patterns using Time Series ML
summary
Hands-on claim: convert Open-Meteo hourly 2024 CSV into line protocol and ingest into InfluxDB processing engine with a Python VM, install neuralprophet on PyTorch, train on three months of multivariate data (temperature + precipitation) and forecast seven days, run as a minute-scheduled trigger that writes forecasts back to a new series. Demo builds a Python plugin and trigger on a laptop; plotting in-demo fails but the forecast write and DB workflow complete.
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chapters (12)
- 0:00Introduction
- 1:29Time series data
- 8:55Database evolution for time series
- 17:24Setting up time series DB
- 21:03Weather data download and prep
- 29:55Installing machine learning libraries
- 37:50DB token and structure
- 41:06Writing data to DB
- 50:08Python plugin for forecasting
- 1:00:37Real-time ML for time series
- 1:07:18Alternative forecasting methods
- 1:13:22Summary
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