The SingleExponentialSmoothing class in the hana_ml.algorithms.pal.tsa.exponential_smoothing module is used to model time series data without trend and seasonality, providing two algorithms: single exponential smoothing and adaptive-response-rate simple exponential smoothing, with various parameters to adjust the model's behavior.
------
Here is a Python code template based on the provided help doc:

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

# Create a SingleExponentialSmoothing instance
sesm = SingleExponentialSmoothing(adaptive_method=False,
                                  accuracy_measure='mse',
                                  alpha=0.1,
                                  delta=0.2,
                                  forecast_num=12,
                                  expost_flag=True,
                                  prediction_confidence_1=0.8,
                                  prediction_confidence_2=0.95)

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

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

Please replace the DataFrame creation part with your actual data. Also, you need to install the `hana_ml` package if you haven't done so. You can install it using pip:

```shell
pip install hana_ml
```