Working with multivariate time series forecasting

With Qlik 프로젝트, you can train machine learning models to forecast time-specific metrics. Using neural network-based methods, models learn and predict complex patterns involving time-specific associations, grouped target data, historical features, and known future variables. To create a time series forecast, prepare a training dataset, use it in a time series experiment, deploy a model, and then create apply datasets that you can use to generate predictions.

Target

As with other experiment types, the target is the column for which you want the model to predict future values. For time series experiments, the target needs to contain numeric data—for example, sales or inventory.

If you are using groups in the time series forecast, models will predict one target value per group per time step in the forecast window. If you are not using groups, your trained models will predict one target value for each time step in the forecast window.

Date index

The date index tracks the time series metrics over a continuous time interval (time step). You need to decide on your time step at an early stage: how often do you need to predict future values?

Specifically, the date index is a column that appears in your training and apply datasets for time series problems. The date index determines the structure of both of these apply datasets—each row represents a step in time (or, with groups, a step in time for each unique grouping).

When you add your training dataset in a time series experiment, possible date index columns are automatically identified and presented to you as Insights at the column level. You can identify them from the Possible date index insight in schema view.

Groups are features containing categorical information for which you want to generate predictions separately. Classic examples of groups include store number and product, which could have been used to organize data for a target such as sales. By selecting store number and product type as groups, your time series models will provide predictions for each individual value across these columns. For example, with a target of sales, if you have three store numbers — 1, 2, and 3 — and two product types — grocery and produce — your model will generate sales predictions for each unique combination of these values.

You should incorporate groups into your time series problem if you have the data and need individual predictions by category. Another advantage of groups is that models can learn globally, better understanding the patterns that exist between the different groupings you define.

You can configure the groups to use for each experiment version. If you do not specify groups but groups are identified in your training dataset, the training will use groups.

Groups are identified by duplicate values in the date index column—for example, for a date of 1/14/2025, you have two records: one for store A, and the other for store B.

Each group in a time series experiment — including the target alone — are considered to be separate time series within your dataset. See What is a time series?.

Primary and secondary groups

For time series problems with two group columns, one group is the primary group and the other is the secondary group. For examples, see Preparing a training dataset, Training dataset example — two groups, and 적용 데이터 집합 예 — 두 그룹.

The primary grouping defines independent time series. For example, each store becomes its own separate time series, allowing the model to learn different behaviors and patterns across stores.

The secondary grouping is treated differently. Instead of creating fully separate series, the system pivots those values into additional features (covariates), allowing related sub-series to provide contextual information to each other.

You generally do not need to know which group is the primary and which is the secondary, although you can optionally choose a primary group in your experiment. Certain considerations apply for predicting with deployed time series models—see Preparing an apply dataset.

You set the forecast window and gap size when configuring an experiment version. These values are used both during model training and when generating predictions from models deployed as ML deployments.

The forecast window is the number of time steps for which you want to predict into the future. For example, if your time step is one day and you want to forecast sales for the next two weeks, you would set your forecast window to 14.

The forecast gap is the amount of time in the future for which you do not require predictions. Setting a forecast gap is optional, because you may or may not need one. The forecast gap starts at the end of the recorded historical training data you have provided. The forecast window begins where the forecast gap ends.

For example, you might be looking to predict future sales, but you are only interested in future sales for dates later than one week after the end of your input data. In this case, with a time step of days, you could set your forecast gap size to seven time steps.

Your selected forecast window, in addition to how much training data you have, limits how far into the future you can forecast. For more information, see Maximum forecast window.

Future features

With future features, you can provide additional data to your models about future information you already know or can reasonably expect. In particular, you have access to future values for this feature spanning your selected forecast horizon. When defining future features, you need to provide historical as well as future data.

For example, for a model predicting metrics that could be influenced by future discounts offered by a store, you could include the historically observed discounts, as well as the discounts for future time periods within the forecast window. Other examples of future features could be weather or calendar information.

Time steps

The time step is defined by your training dataset and is important for both training and predictions.

In your training dataset, the time step is the interval at which the data in your date index is recorded. For example, the time step can be daily, every hour, every minute, or every second. The smallest time step detected is milliseconds.

It is important to be aware of the time step used in your training data. Other experiment parameters you define, such as forecast window and forecast gap size, will follow this time step interval.

After deploying your model, the apply data for which you want to create predictions will need to follow the same time step as defined in the training dataset.

Quality

When you select a training dataset, the system infers the time step used. If there are some missing values or gaps in the date index, columns such as target, groups, and covariates can be interpolated automatically by the system. However, if your data contains time intervals that are inconsistent to the point where different time steps are detected, the data must be fixed first. For example, if you have several months of data recorded once daily, but there is a section in which data is consistently recorded on a weekly basis, the dataset cannot be used because multiple time steps will be detected.

Apply window

The apply window, or look-back period, is the portion of the training data that the algorithm can use to provide the predictions for your specified forecast window.

The apply window is calculated and set by the system. It is measured in time steps. The apply window is defined by what you set as the forecast window and gap (forecast horizon). Your apply window size is shown in the experiment configuration panel and the Model training summary, after running at least one experiment version. It is also shown in an ML deployment Model schema when creating or editing a batch prediction configuration.

The apply window is identified automatically from your training configuration. To generate predictions for a given forecast window, you need to provide the historical data covering at least your apply window. This is provided in your apply dataset. See Preparing an apply dataset.

Maximum forecast window

The maximum forecast window is estimated as you configure your time series experiment. After you have run a version of the training, the maximum forecast window is confirmed with certainty. The maximum forecast window is displayed to you as the Estimated maximum forecast or Maximum forecast under Based on your data, when you open Target and experiment type in the experiment configuration panel. The maximum forecast window is the maximum number of time steps for which you can generate forecasts, given your chosen forecast window, how much historical data you have provided, and the minimum sample size expected by the system. The more historical data you provide, the further in time you will be able to predict. However, to generate reliable predictions, it is important to select a reasonable forecast window.

최대 예측 기간은 180 시간 단계까지 가능합니다.

Forecast cut-off time

The forecast cut-off time is especially important when defining your apply dataset during predictions. The forecast cut-off time is the last date in your sample for which you have a target value. Essentially, dates after this cut-off time are the dates for which you want to generate predictions.

What is a time series?

In Qlik 프로젝트 time series forecasting, each group — including the target alone — are considered to be separate time series within the training dataset. For example, suppose your training dataset contains sales metrics. These sales metrics are defined for each store and product type. With Store and Product Type columns defined as groups, there are three time series in the training dataset.

Preparing a training dataset

For multivariate time series forecasts, your training dataset needs to contain the following columns:

• Date index

• Target column

• Group columns (optional)

• Feature columns (optional—without features, you are training a univariate forecasting model)

Apply dataset — Requirements and validation

For time series models, the apply dataset needs: