# ARIMA Forecast {% hint style="info" %} This tool is currently in Beta and is still being tested. Want to learn more? Like to provide feedback? Please reach out to {% endhint %} *ARIMA*, short for 'AutoRegressive Integrated Moving Average', allows you to forecast a time series using the series past values. ### Input/Output | Inputs | Outputs | | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | | **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. | ### Options | Option | Description | | ------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **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 Options | Option | Description | | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- | | **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. | --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.cascade.io/cascade/tools/data_science/arimaforecast.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.