ARIMA, or Autoregressive Integrated Moving Average, is a class of statistical models utilized for analyzing and forecasting time series data. It is specifically engineered to account for autocorrelation in data points observed over sequential time intervals, making it applicable to datasets where observations depend on preceding values.
Mechanism
The model operates through three distinct components: the Autoregressive (AR) part, which models the dependency between an observation and a fixed number of past observations; the Integrated (I) part, which employs differencing of raw observations to render a non-stationary time series stationary; and the Moving Average (MA) part, which models the dependency between an observation and a residual error from a moving average model applied to lagged observations. The interplay of these components captures linear dependencies.
Methodology
In crypto investing, ARIMA models provide a structured approach for generating short-to-medium term price or volume forecasts for digital assets. The methodology involves identifying the appropriate orders for the AR, I, and MA components (p, d, q) through statistical analysis of historical data, such as autocorrelation and partial autocorrelation functions. This quantitative framework supports algorithmic trading strategies, risk assessments, and institutional options pricing within RFQ crypto environments by predicting future data points based on past patterns.
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