The OnlineARIMA class in the hana_ml.algorithms.pal.tsa.online_algorithms module implements an online learning method to estimate the parameters of ARIMA models by reformulating it into a full information online optimization task, which doesn't depend on noise terms and doesn't require access to the entire large-scale dataset in advance.
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Here is a Python code template based on the provided help doc:

```python
from hana_ml.algorithms.pal.tsa.online_algorithms import OnlineARIMA
from hana_ml import dataframe as df

# Create a connection to your HANA system
cc = df.ConnectionContext(address='<HANA_HOST>', port=<HANA_PORT>, user='<HANA_USER>', password='<HANA_PASSWORD>')

# Create a DataFrame from a HANA table
data = df.DataFrame(cc, 'SELECT * FROM <HANA_TABLE>')

# Initialize the OnlineARIMA model
arima = OnlineARIMA(order=(4,0,8), output_fitted=True, learning_rate=0.00001)

# Fit the model
arima.partial_fit(data=data, key='TIMESTAMP')

# Predict
forecast = arima.predict(forecast_length=10)

# Print the forecast
print(forecast.collect())
```

Please replace `<HANA_HOST>`, `<HANA_PORT>`, `<HANA_USER>`, `<HANA_PASSWORD>`, and `<HANA_TABLE>` with your actual SAP HANA host, port, user, password, and table name respectively.