Building forecasts is an integral part of any business, whether it's revenue, inventory, sales, or customer demand. Building machine learning models is time-consuming and complex with many factors to consider, such as iterating through algorithms, tuning your hyperparameters and feature engineering. These choices multiply with time series data, with additional considerations of trends, seasonality, holidays and effectively splitting training data. Forecasting within automated machine learning (ML) takes these factors into consideration and includes capabilities that improve the accuracy and performance of our recommended models.
For more tips like this, check out the working remotely playlist at www.youtube.com/FoetronAcademy.
Also, if you need any further assistance then you can raise a support ticket (https://cloud.foetron.com/) and get it addressed.
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