All you need to know about the basics of ARIMA

fig 3: First order differencing and fig 4: Second order differencing

Auto Regressive Model

  • Predict future based on past values
  • These models assume that the future will resemble the past
  • Autoregressive models operate under the premise that past values have an effect on current values, which makes the statistical technique popular for analyzing processes that vary over time.
  • AR(1) is the process one in which the current value is based on the immediately preceding value,
  • AR(2) process is one in which the current value is based on the previous two values.
  • An AR(0) process is used for white noise and has no dependence between the terms.
  • An autocorrelation of +1 represents a perfect positive correlation, while an autocorrelation of negative 1 represents a perfect negative correlation.
  • The direct and indirect effect of values in the previous time lags is there.
    Eg: If you want to compare the marks of 8th std and 12th std. There could be many indirect effects also, like.. 8th std depends on 9th, and 9th depends on 10th and so on. These are all indirect effects.
  • Partial autocorrelation has only the direct effect of values in the previous time lags. Eg: Here we are only interested to check if 12std marks have a direct correlation with 8th std or not.

Moving Average


Autoregressive Integrated Moving Averages

  • (ARIMA) models predict future values based on past values.
  • ARIMA makes use of lagged moving averages to smooth time series data.
  • ARIMA is a form of regression analysis that gauges the strength of one dependent variable relative to other changing variables.
  • There are 2 components in ARIMA:
  1. Autoregression (AR): refers to a model that shows a changing variable that regresses on its own lagged, or prior, values.
  2. Integrated (I): represents the differencing of raw observations to allow for the time series to become stationary (i.e., data values are replaced by the difference between the data values and the previous values).
  3. Moving average (MA): incorporates the dependency between an observation and a residual error from a moving average model applied to lagged observations.




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Praseeda Saripalle

Praseeda Saripalle

Data Science Aspirant

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