# Forecast with Time

Predict with Time Series generates predictions on your dataset given a target and a temporal column.

- Predict Time Series.
`Predict`

using a form or the chat box. - Univariate Analysis. Prediction with one measure variable.
- Multivariate Analysis. Prediction with one measure variable and another variable that affects the measure variable.
- Multiple Time Series. Predictions with multiple measure variables or with a grouping variable.
- Group Temporal Repetitions. Adjust datasets with multiple values per temporal variable.

## Predict Time Series

To use the `Predict Time Series`

form, select **ML > Predict Time Series** in the sidebar.

The **Predict Time Series** form appears.

- Select at least one column that contains measure variables.
- Enter the number of values to predict.
- Select the column that contains your temporal variable.
- Optionally, select a column that contains a variable that groups your data for better predictions.
- Click
**Submit**.

DataChat applies an appropriate forecasting method to generate the specified number of predicted values for the measure column. A new dataset is created that includes the predicted values. If only one measure variable is specified, the univariate analysis generates a new dataset: "PredictedTimeSeries_<measure variable>". If more than one measure variable is specified—multiple time series, which can be either univariate or multivariate— the new dataset is named "PredictedTimeSeries".

The current dataset is set to the new, generated dataset. To run a different analysis on the initial dataset, set the current dataset to the initial dataset.

The new dataset is used to generate a visualization, which displays as a popup by default. After you close the popup, the visualization appears in the chart panel.

To build a DataChat sentence in the chat box, see `Predict Time Series`

.

## Univariate Prediction

A univariate analysis involves a single measure variable for each time variable. A univariate time series prediction requires:

- At least one measure column that contains target values to predict.
- The number of time intervals to predict.
- One temporal column that contains time interval variables, of date/time type, with one measure variable per time interval.

If DataChat detects multiple measure variables per time interval, you are prompted to group temporal repetitions.

You can use the Predict Time Series form, or enter a DataChat sentence in the chat box. For example, with the measure column "positiveIncrease" and the time interval "date":

`Predict time series with measure columns positiveIncrease for the next 7 values of date`

Upon success:

The chat box includes a dropdown of the model scores to provide context about the success of the prediction model:

- SMAPE.
- Mean absolute error.

The chart's legend displays:

- Blue circles. The data points for the specified measure variable.
- Gold stars. The predicted values for the specified number of time intervals.
- Gray area. Confidence interval for time series prediction.

If you specify multiple time series DataChat includes links to other charts.

## Multivariate Prediction

A multivariate analysis involves at least two variables for each time variable. A multivariate time series prediction requires:

- At least one measure column that contains target values to predict. More than one column generates multiple time series.
- The number of time intervals to predict.
- One temporal column that contains time interval variables, of date/time type.
- At least one column that could influence the measure column.

If DataChat detects multiple measure variables per time interval, you are prompted to group temporal repetitions.

Enter a DataChat sentence in the chat box. For example, with the measure column "Total_Counts", the time interval "Time", and two columns that could influence the measure column, "Bike_North" and "Ped_North":

`Predict time series with measure columns Total_Counts for the next 6 values of Time using the columns Bike_North, Ped_North`

Upon success:

The chat box includes a dropdown of the model scores to provide context about the success of the prediction model:

- SMAPE.
- Mean absolute error.

Click

**here**to run a univariate version of`Predict Time Series`

, without`using the columns <columns>`

.DataChat provides a link to run a univariate analysis – without the selected columns that could influence the measure column, The link appears with a successful multivariate analysis as well as if the specified variables are unsuitable for multivariate analysis – for example because there isn't enough data.

The chart displays the multivariate time series prediction. The chart's legend displays:

- Blue circles. The data points for the specified measure variable.
- Gold stars. The predicted values for the specified number of time intervals.

If you specify multiple time series, DataChat includes links to other charts.

## Multiple Time Series

You can run multiple time series analyses if you either select more than one measure variable or specify a grouping variable. DataChat runs a separate time series analysis—either univariate or multivariate—for each variable. Every variable must share the same time axis.

You can use the Predict Time Series form for univariate prediction. Enter a DataChat sentence in the chat box using the `Predict Time Series`

skill for either univariate or multivariate prediction.

For example, given a dataset with columns "Total_Counts", "Bike_North", "Ped_North", and the time interval "Time":

Generate two univariate time series predictions with two measure variables.

`Predict time series with measure columns Bike_North, Ped_North for the next 6 values of Time`

For each variable (univariate), DataChat applies the best-fitting model to generate a prediction. Upon success, DataChat generates:

- Datasets for each predictive analysis.
- A chart of the first prediction.
- Model scores.
- Links to charts of predictions for other specified variables.

Other examples:

Generate two univariate time series predictions with a grouping variable.

`Predict time series with measure columns Total_Counts for the next 6 values of Time for each Bike_North`

Generate multiple multivariate time series predictions with one grouping variable and another variable that might affect the measure variable.

`Predict time series with measure columns Total_Counts for the next 7 values of Time for each Bike_North using the columns Ped_North`

## Group Temporal Repetitions

If DataChat detects that the column containing your temporal variable has at least one repeated value, meaning that at least two rows contain different measure variables for the same temporal variable, DataChat will prompt you with options to aggregate the measure variables. You can choose by clicking one of the following options, displayed as links:

- Average
- Maximum
- Median
- Minimum
- Total

For each duplicated temporal variable, the measure variables are aggregated as selected in a new dataset called `<dataset name>_Compute>`

. Run `Predict Time Series`

on the new dataset. For more options, see the `Predict Time Series`

skill.