The DoubleExponentialSmoothing class in the hana_ml.algorithms.pal.tsa.exponential_smoothing module is used to model time series data with a trend but without seasonality, using parameters such as alpha and beta for smoothing and trend components, forecast_num for the number of values to be forecast, and options for damped trend method, accuracy measure, and prediction confidence intervals.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.tsa.exponential_smoothing import DoubleExponentialSmoothing

# Create a DoubleExponentialSmoothing instance
desm = DoubleExponentialSmoothing(alpha=0.501,
                                  beta=0.072,
                                  forecast_num=6,
                                  phi=None,
                                  damped=None,
                                  accuracy_measure='mse',
                                  ignore_zero=None,
                                  expost_flag=None,
                                  prediction_confidence_1=0.8,
                                  prediction_confidence_2=0.95)

# Perform fit_predict() on the given data
desm.fit_predict(data=df)

# Output
print(desm.forecast_.collect().set_index('TIMESTAMP').head(3))
print(desm.stats_.collect())
```

Please replace the DataFrame `df` with your actual data. The `fit_predict()` function fits the model to the data and then makes a prediction. The results are stored in the `forecast_` and `stats_` attributes of the `DoubleExponentialSmoothing` instance.