Time-series models are best suited for forecasting tasks where the data points are collected over time and exhibit patterns such as trends, seasonality, and autocorrelation. They are particularly effective when the focus is on capturing the temporal dependencies and dynamics inherent in the time series data. Time-series models like ARIMA, SARIMA, Holt-Winters, and LSTM are commonly used in the following scenarios:
Demand Forecasting, Financial Forecasting, Energy Consumption Forecasting, Weather Forecasting and Healthcare Forecasting.
Machine learning (ML) based models, on the other hand, are suitable for forecasting tasks where the data exhibit complex relationships, non-linear patterns, and high-dimensional feature spaces. ML models like random forest, XGBoost, and neural networks excel in capturing intricate patterns and making accurate predictions based on diverse sets of input features. ML based models are commonly used in the following scenarios:
Demand Modelling, Customer Churn Prediction, Marketing Analytics, Predictive Maintenance, and Risk Assessment