ARIMA and LSTM time series forecasting models require data to be in chronological sequence. ARIMA works well for datasets with clear historical trends that require stationarity, whereas LSTM works well for capturing more complicated interactions that go beyond linear or seasonal patterns. When dealing with high-dimensional datasets that require numerical inputs and careful feature scaling, Support Vector Machines (SVM) are useful. SVMs are very useful when classes are clearly separated. Because they require numerical inputs and scaled features, neural networks excel at managing complicated and huge datasets where traditional methods may fall short. They are appropriate for cases with complex variable relationships.

ARIMA and LSTM can model and predict response times based on past patterns when forecasting response time and resource requirements. SVMs can categorize occurrences based on expected reaction times or resource requirements, but neural networks excel at complicated prediction tasks including a plethora of influencing elements in historical data. The model chosen is determined by unique data features and the nature of the forecasting activity.