Nn models sets7/10/2023 If you have hourly data and you expect your data exhibits weekly seasonality, you should have more than 7*24 = 168 observations to train a model. For most time series applications, this means that the submitted data should have as many observations as the period of the maximum expected seasonality.įor example, if you have daily sales data and you expect that it exhibits annual seasonality, you should have more than 365 data points to train a successful model. In time series forecasting, there is a general rule of thumb that a decent model should always have more observations than parameters in the time series. For most time series applications, this means that the submitted data should have as many observations as the period of the maximum expected seasonality. In time series forecasting there is a general rule of thumb that a decent model should always have more observations than parameters in the time series.
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