This time series has a single dynamic that can be learned by a local model which only looks at the historic data to make future predictions.
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Part 1 of an exploratory series on the TimeGPT model from Nixtla. We'll take a look at how the model works and how it changes the way we think about common forecasting problems.
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Statistical, machine learning, and deep learning forecasting approaches each have their own unique pros and cons
Nousot's demand forecasting solution splits your data into four major buckets in order to model each one separately.
This transformation applies a Kalman filter to smooth data. Source: https://github.com/winedarksea/AutoTS/blob/master/autots/tools/transform.py#L3467