Other model types consider all of the model types not already considered. These generally will also do well at handling non-linear trends, but are computationally much more expensive as the dataset size increases.
Other models used in Qlik AutoML:
|
Other |
KNearestNeighborsRegressor |
Regression |
|
Other |
KNearestNeighborsClassifier |
Classification |
|
Other |
SupportVectorMachineRegressor |
Regression |
|
Other |
SupportVectorMachineClassifier |
Classification |
A note about univariate time series:
There are several ways to approach a time series. Each comes with its own caveats. Univariate time series uses only time and a value, let’s say revenue. Even in univariate time series, there are several different algorithms, but the more complex ones parse the date to look for different cyclical trends like daily, weekly, monthly, quarterly, yearly seasonality trends. The benefit of this is that you can forecast out to any date because you can always know the date. The downside to this is that it is only looking at the cyclical growth patterns in that single value, but if revenue is highly dependent on marketing spend, and the business decides to change strategy and slow or increase marketing spend, the time series model will not effectively pick that up and will forecast on the historical trend of marketing spend. AutoML uses a model very similar to ARIMA for time series, which can do a good job, but for many use cases, regression may be a better option.