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# Autoregressive Integrated Moving Average (ARIMA) Algorithm

Autoregressive Integrated Moving Average (ARIMA`Package 'forecast'`

of the Comprehensive R Archive Network
(CRAN).

## How ARIMA Works

The ARIMA algorithm is especially useful for datasets that can be mapped to stationary time series. The statistical properties of stationary time series, such as autocorrelations, are independent of time. Datasets with stationary time series usually contain a combination of signal and noise. The signal may exhibit a pattern of sinusoidal oscillation or have a seasonal component. ARIMA acts like a filter to separate the signal from the noise, and then extrapolates the signal in the future to make predictions.

## ARIMA Hyperparameters and Tuning

For information about ARIMA hyperparameters and tuning, see the `Arima`

function documentation in the Package
'forecast'

Amazon Forecast converts the `DataFrequency`

parameter specified in the CreateDataset operation to the
`frequency`

parameter of the R ts

DataFrequency (string) | R ts frequency (integer) |
---|---|

Y | 1 |

M | 12 |

W | 52 |

D | 7 |

H | 24 |

30min | 2 |

15min | 4 |

10min | 6 |

5min | 12 |

1min | 60 |

For frequencies less than 24 or short time series, the hyperparameters are set using the
`auto.arima`

function of the `Package 'forecast'`

of CRAN

Supported data frequencies that aren't in the table default to a `ts`

frequency
of 1.