Modeling time series with ARIMA class models
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. That is because, in contrast to the exponential smoothing models, the ARIMA models require the time series to be stationary. Below we briefly go over the models’ building blocks.
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, refers to differencing the original time series (subtracting the value from the previous period from the current period's...
