ARIMA is one of the most popular traditional statistical methods used for time series forecasting.
Let's understand its components 🧵 👇
Let's understand its components 🧵 👇
ARIMA stands for Auto-Regressive Integrated Moving Average.
It is composed of 3 components:
🔹 Auto-Regressive (AR)
🔹 Integrated (I)
🔹 Moving Average (MA)
It is composed of 3 components:
🔹 Auto-Regressive (AR)
🔹 Integrated (I)
🔹 Moving Average (MA)
1️⃣ Auto-Regressive (AR) models use a linear combination of past values of the variable of interest.
They are described by the parameter "p", which refers to the number of previous values to consider for the forecast.
They are described by the parameter "p", which refers to the number of previous values to consider for the forecast.
2️⃣ Moving Average (MA) models use a linear combination of past error values instead of previous values of the variable of interest.
They are characterized by the parameter "q", which refers to the number of previous error values to consider for the forecast.
They are characterized by the parameter "q", which refers to the number of previous error values to consider for the forecast.
3️⃣ Integrated (I) refers to the differentiating of the time series data.
It's characterized by the parameter "d", which refers to the number of times differencing is applied to achieve a stationary time series.
It's characterized by the parameter "d", which refers to the number of times differencing is applied to achieve a stationary time series.
ARIMA models extend the applicability of the AR, MA or ARMA (without the I) models to non-stationary data, since those are valid only for stationary time series.
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