Limitations of Machine Learning – What the Forecast App Can't Do
Although the Forecast App is 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 Forecast Without a Pattern
Problem: If your data contains no recognizable pattern, the model cannot find one either.
Example: A target attribute fluctuates purely randomly or contains no relevant correlation to the input values—in this case, the Forecast App can only estimate average values, but cannot make reliable predictions.
Recognizable by:
Flat prediction curves
Low model improvement after retraining
Very high or very low confidence interval in the output
Solution:
First, check in Analytics & Reports whether recognizable trends or patterns exist.
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 severely distort the model structure and prediction behavior.
Example: If all values are in the range of 0.01, but a data point suddenly amounts to 10,000,000, the model will artificially raise future predictions to account for possible "similar" outliers.
Solution:
Clean historical data before training (e.g., with filters or manual removal).
If necessary, use logarithmic or normalized values.
Use the Eliona Calculator to derive differentiated or smoothed values.
3. New Value Ranges Lead to Inaccurate Forecasts
Problem: If values suddenly occur after training that the model has never seen before, it cannot learn a sensible response to them.
Example: A sensor provides values between 10–50 during training. After training, new values in the range of 100–200 arrive. The model does not "know" this range and behaves unpredictably.
Solution:
Instead of predicting absolute values, calculate relative changes or differences.
In case of changes in data behavior: Retrain the model.
4. Models Need Sufficient Data
Problem: If there is too little data, a viable model cannot be created.
Recommendation:
At least several hundred data points should be available.
Ideally: Histories with seasonal or periodic fluctuations over several cycles.
6. Frequent Structural Breaks in the Data History
Problem: Sudden changes (e.g., system changes, 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, but only statistical patterns.
The model cannot:
Independently recognize faulty data
"Understand" or interpret decisions
Deal with completely unknown data ranges in a meaningful way
Make predictions if no explainable patterns exist
But the model can:
Recognize regularities
Derive reliable trends from historical patterns
Provide forecasts based on consistent, cleaned data
Good forecasting doesn't start with the model—it starts with the data.
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