# Understanding Forecast Parameters in Detail

## Key Parameters for the *Forecast App*

In BuildingPro Suites's *Forecast App*, three parameters determine the quality and meaningfulness of your time series predictions. Below you will find a compact description of the most important settings for each:

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## 1. Forecast Length

**Definition:** The number of time steps the model should predict into the future.

* **Calculation of the time distance:** The app automatically determines the average time interval of your most recently saved data points in BuildingPro Suites.
* **Examples:**
  * With 5-minute intervals and `[forecast_length] = 3`, the forecast corresponds to a horizon of 15 minutes.
  * With daily measured values and `[forecast_length] = 5`, five days are predicted.

{% hint style="warning" %}
Pay attention to regular data intervals. With irregular storage, the timeline cannot be derived correctly. It is recommended to activate the "always" trending option in the attribute.
{% endhint %}

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## 2. Context Length

**Definition:** The number of past data points that the model uses as historical context.

* **Short-term patterns:** Small values (e.g., 24) are suitable for daily cycles or hourly trends.
* **Long-term effects:** Larger values (e.g., 168 or more) are useful when seasonal or weekly patterns are to be recognized.
* **LSTM advantage:** Thanks to the *Long Short-Term Memory* architecture, the model remembers relevant information even beyond the selected window, which usually means a moderate window size is sufficient.

{% hint style="warning" %}
Longer context windows dramatically increase the computing effort and memory requirements.
{% endhint %}

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## 3. Feature Attributes

**Definition:** *Feature Attributes* are additional input variables that the model uses to calculate the forecast. They expand the context and thus improve the accuracy of the prediction.

### Automatic Time Features:

If no other feature is selected, the *Forecast App* automatically adds the following time-based attributes:

* `minute_of_hour`
* `hour_of_day`
* `day_of_week`
* `month_of_year`

These fields automatically appear in the form when a forecast item is created and provide the model with cyclical information about the current time structure.

The actual model processing is done internally via the transformed variants of these values (`_sin`/`_cos`), e.g.:

* `hour_of_day_sin` / `hour_of_day_cos`
* `day_of_week_sin` / `day_of_week_cos`
* `month_of_year_sin` / `month_of_year_cos`

This transformation ensures that cyclical time patterns (e.g., the transition from hour 23 to 0) are correctly mapped mathematically.

### Optional: Asset-related Features

You can add other attributes of the same asset, e.g.:

* Temperature
* Humidity
* Energy consumption

These help the model recognize relationships between different measured variables.

### Best Practices:

* **Do not use meter readings directly.** Instead, insert differentiated or relative values.
* **Create your own features:** Use the *Eliona Calculator* to generate combined or derived values (e.g., area calculation, growth rates).
* **Secure data quality:** Use filters to remove outliers or smooth noisy signals.