The intermittent_forecast function in the hana_ml.algorithms.pal.tsa.intermittent_forecast module is a forecasting strategy for products with intermittent demand, providing an exponential weight to estimate, not requiring the initial value of non-zero demands and time interval between non-zero demands, and allowing for various parameters to be specified or optimized, returning a tuple of two DataFrames with forecast values and related statistics.
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Here is the executable code template for the `intermittent_forecast` function:

```python
from hana_ml.algorithms.pal.tsa import intermittent_forecast

# Apply intermittent_forecast
forecasts, stats = intermittent_forecast(
    data=data, 
    p=3, 
    forecast_num=3,
    optimizer='lbfgsb_grid', 
    grid_size=20,
    optimize_step=0.011, 
    expost_flag=False,
    accuracy_measure='mse', 
    ignore_zero=False,
    thread_ratio=0.5
)

# Print the forecast values
print(forecasts)

# Print the related statistics
print(stats)
```

Please replace the `data` DataFrame with your actual data and adjust the parameters of the `intermittent_forecast` function according to your needs.