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Time Series Forecasting
Auto ARIMA
Auto ARIMA automatically identifies and fits the best ARIMA model to univariate time series data, optimizing for accuracy and efficiency.
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Automated ARIMA model selectionTime series forecasting
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About Auto ARIMA
Auto ARIMA, part of the 'forecast' package in R, automates the process of identifying and fitting the most suitable ARIMA (Autoregressive Integrated Moving Average) model to univariate time series data. It conducts a comprehensive search across possible models, constrained by user-defined order limits. The function selects the best model based on information criteria like AIC, AICc, or BIC, balancing model complexity and goodness-of-fit. Stepwise selection is employed for faster computation, or a full search is conducted for optimal results. Auto ARIMA handles both stationary and non-stationary data, considering seasonal components if present. It offers options for Box-Cox transformation, drift terms, and parallel processing to enhance performance. It's designed for analysts and researchers seeking efficient and accurate time series forecasting without manual model selection.
Core Capabilities
Auto ARIMA, part of the 'forecast' package in R, automates the process of identifying and fitting the most suitable ARIMA (Autoregressive Integrated Moving Average) model to univariate time series data.
