ARIMA Forecast
Time-series forecasting
Last updated
Time-series forecasting
Last updated
This tool is currently in Beta and is still being tested. Want to learn more? Like to provide feedback? Please reach out to support@cascade.io
ARIMA, short for 'AutoRegressive Integrated Moving Average', allows you to forecast a time series using the series past values.
| Inputs | Outputs |
|---|---|
| Option | Description |
|---|---|
| Option | Description |
|---|---|
Time Series Table - Time series dataset that includes a date column and at least one numerical column.
Table with a new column per forecasted time series values based on the time series input.
Date/Time Column
Column to be used as the date time reference for the forecast.
Forecast Columns
Selection of columns to be used to during training and prediction of the ARIMA forecast model.
Select all numerical fields
Replaces the "Forecast Columns" prompt above (cannot select both). Instead of selecting individual columns in your input table, choosing this option will automatically select all columns with numerical formats
Interval
Time frequency of the date/time column specified.
Train-Test Split
Defines the split between training and test data.
Periods to Forecast
Number of periods to forecast, starting from train-test split.
Tuning Parameters
When set to auto, the ARIMA model will choose parameters automatically. When set to manual, you will have access to specify the ARIMA model parameters.
p
The number of lag observations included in the model, also called the lag order.
d
The number of times that the raw observations are differenced, also called the degree of differencing.
q
The size of the moving average window, also called the order of moving average.
P
The number of lag observations included in the seasonal part of the model, also called the lag order.
D
The number of times that the raw observations are differenced, also called the degree of differencing, for the seasonal part of the model.
Q
The size of the moving average window, also called the order of moving average, for the seasonal part of the model
s
Seasonal differencing--refers to the number of periods in each season.
Prediction Interval
Interval for the testing significance. Specify values 0.0 to 1.0.