ARIMA models are a class of statistical models that are used for analyzing and forecasting time series data. They aim to do so by describing the autocorrelations in the data. ARIMA stands for Autoregressive Integrated Moving Average and is an extension of a simpler ARMA model. The goal of the additional integration component is to ensure stationarity of the series, because, in contrast to the exponential smoothing models, the ARIMA class requires the time series to be stationary. In the next few paragraphs, we briefly go over the building blocks of ARIMA models.
AR (autoregressive) model:
- This kind of model uses the relationship between an observation and its lagged values.
- In the financial context, the autoregressive model tries to account for the momentum and mean reversion effects.
I (integration):
- Integration, in this case,...