No, ARIMA model is not suitable for every type of time series problem. There are situations where ARMA model and others also come in handy.
ARIMA is best when different standard temporal structures require to be captured for time series data.
No, the ARIMA (AutoRegressive Integrated Moving Average) model may not be a good fit for every time series problem. ARIMA models are effective for stationary time series data, where the statistical properties such as mean and variance do not change over time. However, many real-world time series data exhibit trends, seasonality, or other complex patterns that may violate the stationarity assumption.
In cases where time series data are non-stationary, transformation techniques like differencing or other more sophisticated models such as SARIMA (Seasonal ARIMA) or machine learning models like SARIMA (Seasonal ARIMA) or machine learning models like Long Short-Term Memory (LSTM) networks for deep learning may be more appropriate. It’s essential to understand the characteristics of the specific time series data and choose a model that can capture its underlying patterns effectively.