Reference Updated April 8, 2026

ARIMA

Category: Rollforward & time series

Overview

Description	Forecasting function based on the autoregressive integrated moving average (ARIMA) model. Use when you want to forecast a time series from historical patterns without defining explicit driver nodes. Learn more
Syntax	`ARIMA('Node', p, d, q, P, D, Q, m)`
Parameters	Node: Node that contains the base data for the forecast. It must contain at least one time level. Predictions are based on the time values in the node and extend to the end of the horizon period. If the input data has a missing value in the time dimension, the calculation fails. A time value is considered missing if the data has actual value(s) before and after the missing value. For example, `[2019, 2021]` has missing value `2020`, but `[2020, 2021]` or `[2019, 2020]` have no missing values. p: the order (number of time lags) of the autoregressive model d: the degree of differencing q: the order of the moving-average model P: autoregressive term for the seasonal part D: differencing term for the seasonal part Q: moving average term for the seasonal part m: the number of periods in each season

Description

Forecasting function based on the autoregressive integrated moving average (ARIMA) model.

Use when you want to forecast a time series from historical patterns without defining explicit driver nodes.

Learn more

Syntax

`ARIMA('Node', p, d, q, P, D, Q, m)`

Parameters

Node: Node that contains the base data for the forecast. It must contain at least one time level.
Predictions are based on the time values in the node and extend to the end of the horizon period.
If the input data has a missing value in the time dimension, the calculation fails. A time value is considered missing if the data has actual value(s) before and after the missing value.
For example, [2019, 2021] has missing value 2020, but [2020, 2021] or [2019, 2020] have no missing values.
p: the order (number of time lags) of the autoregressive model
d: the degree of differencing
q: the order of the moving-average model
P: autoregressive term for the seasonal part
D: differencing term for the seasonal part
Q: moving average term for the seasonal part
m: the number of periods in each season

Examples

Monthly forecast from historical data

This example shows monthly historical passenger data used as the basis for an ARIMA forecast. The result keeps the historical observations and extends the series with forecasted periods to the end of the horizon.

Input node: Passenger historic

Data set: Passenger historic.xlsx )

Data horizon: 2000 - 2011

Year	Month	Value
2000	2000-01	112
…	…	…
2011	2011-12	432

Formula: ARIMA('Passenger historic', 1, 1, 1, 1, 0, 1, 12)

ARIMA horizon: 2000 - 2020

Year	Month	→ ARIMA Result
2000	2000-01	112
…	…	…
2011	2011-12	432
2012	2012-01	450
…	…	…
2020	2020-12	861

Chart:

Function	When to use instead
ROLLFORWARD_ADVANCED	When you want to project values with explicit driver nodes instead of forecasting from historical patterns alone.
MOVINGAVG	When you want to smooth historical values over a rolling window instead of creating a forecast.

Overview

Examples

Monthly forecast from historical data

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