Define ARIMA in simplest terms
Question
TheDataMonk
Follow Me
About TheDataMonkGrand Master
I am the Co-Founder of The Data Monk. I have a total of 6+ years of analytics experience 3+ years at Mu Sigma 2 years at OYO 1 year and counting at The Data Monk I am an active trader and a logically sarcastic idiot :)Follow Me
Answers ( 4 )
ARIMA is the model used in forecasting problems, that means when you have data for
past several days, months, years etc and you need to predict future values based on the past values.
ARIMA is composed of 3 parts AR, I and MA.
AR —> Autoregressive component
I —> Integrated
MA —> Moving Average.
AR –> It’s value depend upon how many past values are contributing in determining the present value.
It can be found from the PACF plot.
I —> It’s value depend upon how many times you need to take differences of the values in a time series
to make it stationary.
MA —> Its’s value depends upon how many error terms corresponding to previous lags values are
contributing in determining the present value. It can be found from the ACF plot.
ARIMA stands for Auto Regressive Integrated Moving Average. ARIMA is a simple stochastic time series model that we can use to train and then forecast future time points.
ARIMA can capture complex relationships as it takes error terms and observations of lagged terms. These models rely on regressing a variable on past values.
ARIMA stands for Auto Regressive Integrated Moving Average model. It is like a linear equation to forecast time series data. Three parameters that we need to specify while training ARIMA are:
1. Auto Regressive (p) : This is the number of previous terms used to predict the current term
2. Integrated (d) : Number of times we need to take the difference to make the time series stationary. Most common value of d is 1, which means x(t) = x(t) – x(t-1) , where x is the value and t is the current time stamp.
3. Moving Average (q) : Number of previous error terms considered while making prediction. Error term at each step is calculated by getting the difference b/w actual value and moving averages at that step.
ARIMA, short for ‘AutoRegressive Integrated Moving Average’, is a forecasting algorithm based on the idea that the information in the past values of the time series can alone be used to predict the future values.
Any ‘non-seasonal’ time series that exhibits patterns and is not a random white noise can be modeled with ARIMA models.
An ARIMA model is characterized by 3 terms: p, d, q
where,
p is the order of the AR term
q is the order of the MA term
d is the number of differencing required to make the time series stationary
‘p’ is the order of the ‘Auto Regressive’ (AR) term. It refers to the number of lags of Y to be used as predictors.
‘q’ is the order of the ‘Moving Average’ (MA) term. It refers to the number of lagged forecast errors that should go into the ARIMA Model.
The value of d, therefore, is the minimum number of differencing needed to make the series stationary. And if the time series is already stationary, then d = 0.