# Limits of machine learning – What the Forecast app cannot do

Although the *Forecast App* based on powerful LSTM models, it is – like any machine learning method – only as good as the data it works with. This chapter highlights typical pitfalls and misconceptions that can lead to poor or misleading results when using the app.

***

## 1. No prediction without a pattern

**Problem:** If your data does not contain a recognizable pattern, the model cannot find one either.

**Example:**\
If a target attribute fluctuates purely at random or contains no relevant correlation to the input values, then the *Forecast App* can only estimate average values, but cannot make reliable predictions.

**Recognizable by:**

* Flat prediction curves
* Little model improvement after retraining
* Very high or very low confidence interval in the output

**Solution:**

* Check in advance in *Analytics & Reports*, whether recognizable trends or patterns are present.
* Avoid binary, random, or irregular signals without explainable dependencies.

***

## 2. Extreme values and outliers disrupt the model

**Problem:** A single outlier (e.g. due to a faulty measurement) can strongly distort the model structure and prediction behavior.

**Example:**\
If all values are in the range of 0.01, but one data point suddenly is 10,000,000, the model will artificially raise future predictions to account for possible "similar" outliers.

**Solution:**

* Clean historical data before training (e.g. by filtering or manually removing).
* Use logarithmic or normalized values if necessary.
* Use the *Eliona Calculator*to derive differentiated or smoothed values.

***

## 3. New value ranges lead to inaccurate forecasts

**Problem:** If values suddenly appear after training that the model has never seen before, it cannot learn any meaningful response to them.

**Example:**\
A sensor delivers values between 10–50 during training. After training, new values in the range 100–200 appear. The model does not "know" this range and behaves unpredictably.

**Solution:**

* Instead of predicting absolute values, calculate relative changes or differences.
* If the data behavior changes: retrain the model.

***

## 4. Models require sufficient data

**Problem:** If there is too little data, no viable model can be created.

**Recommendation:**

* At least several hundred data points should be available.
* Ideal: histories with seasonal or periodic fluctuations over several cycles.

{% hint style="info" %}
The system automatically checks the data length and starts training only when enough measurements are available.
{% endhint %}

***

## 6. Frequent structural breaks in the data trend

**Problem:** Sudden changes (e.g. system conversions, changes in measurement methods) lead to structural breaks that the model cannot explain.

**Solution:**

* Segment the dataset before training.
* Avoid mixing different data sources or measurement methods in a forecast.

***

## Conclusion

The *Forecast App* is a powerful tool – but only with sensibly prepared and structured data. It does not recognize meaning, only statistical patterns.

**The model cannot:**

* Independently detect faulty data
* "Understand" or interpret decisions
* Deal sensibly with completely unknown data ranges
* Make predictions when no explainable patterns exist

**But the model can:**

* Recognize regularities
* Derive reliable trends from historical patterns
* Provide forecasts based on consistent, cleaned data

{% hint style="success" %}
Good forecasting does not start with the model – it starts with the data.
{% endhint %}