Forecasting with machine learning involves applying serious compute power to vast data, using AI to predict future values.
Time-series models need uniform time interval data for accurate analysis; irregular data can be sampled for improved forecasting.
Experiment with various data sampling intervals and methods to enhance the performance of time-series models for better forecast accuracy.
In the tutorial, a time-series model will be trained on historical IoT sensor data to forecast air temperatures by learning patterns and correlations between recorded values.
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