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In this tutorial, we use the chicago_weather_stations dataset. It contains data collected by the sensors at different timestamps. Note that this data lacks uniform time intervals between consecutive entries, as shown below.
If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models.
If your data lacks uniform time intervals between consecutive entries, QuestDB offers a solution by allowing you to sample your data. After that, MindsDB facilitates creating, training, and deploying your time-series models.
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