Start forecast & training

Forecast Details

Element	Title	Details
[A]	General Configuration	Parent block with all settings for the forecast model.
[A1]	asset	The selected asset whose data is used for the forecast.
[A2]	Target Attribute	Attribute for which the prediction is calculated.
[A3]	Feature Attributes	List of additional attributes (minute, hour, day of week …); they serve as input features for the model.
[A4]	Forecast length	Number of time steps forecast into the future.
[A5]	Context length	Length of the historical context window that the model takes into account per prediction.
[A6]	start/stop training	Switch for starting/stopping the training run.
[A7]	start/stop forecasting	Switch for enabling/disabling the ongoing forecast.
[B]	Info	Status panel with runtime information.
[B1]	Forecast Status	Shows whether the forecast is active/inactive and, if active, in which state the forecast is.
[B2]	Training Status	Shows whether training is active/inactive and in which state the training is.
[B3]	DOC	Link to the online documentation of the Forecastapp.
[C]	Forecast Chart	Chart with actual and forecast values.
[C1]	Legend – actual value	Marks the measured sensor data (Actual humidity).
[C2]	Legend – forecast	Marks the calculated predictions (Forecast humidity).
[C3]	Forecast line	Shows the forecast as a line in the chart.

Start forecast & start training

The Forecast App allows model training and ongoing forecasting to be activated independently of each other. Both processes run in parallel in the background, but follow different tasks and workflows. The following sections explain how it works, dependencies, and the recommended procedure.

Start training

Goal: Trains a new LSTM model based on the currently available historical data.

Process:

• The training process is started via the switch Train [A6] .

• The model is trained with the current settings for forecast_length, context_length and feature_attributes .

• During training, the best model each time (measured by the Validation Loss) is automatically cached.

Important:

• The training status is displayed in the Info panel displayed.

• After successful completion, the following message appears: → Training completed successfully. Waiting for enough data to retrain.

Automatic re-train: After the initial training, the model is automatically retrained as soon as at least 10% more data points are available than in the last run. → Example: If a model was trained on 10 months of data, the next training starts as soon as one more month of data has been added.

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Start forecast

Goal: Creates ongoing predictions for new incoming data points based on the last trained model.

Process:

• The forecast is activated via the switch Forecast [A7] .

• The app checks whether a trained model is available. Only then can the prediction begin.

• New forecast values are automatically generated and stored as an attribute in the asset (see → Forecast output attribute).

Note: If no model is available yet (e.g. after the initial setup), then no prediction can be made. In this case, the status shows: → Waiting for model to be trained.

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Recommended order

To achieve reliable results, it is recommended to:

1. First start the training

2. After successful completion, then activate the forecast

This avoids initial predictions being based on an untrained or poorly converged model.

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Technical terms in the status area

Term	Meaning
Epochs	Number of complete training passes over the entire dataset.
Validation Loss (val_loss)	Error metric on the validation data. Shows how well the model generalizes. The lower, the better.

These values help assess the quality of the training process.

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Forecast output attribute

When starting the forecast or training process, the Forecast App automatically creates a new attribute in the selected asset. This attribute contains the calculated forecast values and is available system-wide.

Naming scheme: [target_attribute]_forecast_[forecast_length]

Example: If the attribute temperature with a forecast_length of 12 is forecasted, the name of the automatically created output attribute is: → temperature_forecast_12

Note: This attribute can be used anywhere in Eliona — e.g. in dashboards, rules, visualizations, or the Calculator. It behaves like a regular asset attribute and is automatically updated with the latest forecast values.