Can you provide Python sample code for real-time forecasting

python
import pandas as pd
from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_squared_error

# Load the time series data
data = pd.read_csv('your_data.csv', index_col='date', parse_dates=['date'])

# Split the data into training and testing sets
train = data[:-10]
test = data[-10:]

# Fit the ARIMA model on the training data
model = ARIMA(train, order=(1,1,1))
model_fit = model.fit()

# Make real-time forecasts
forecast = model_fit.forecast(steps=10)[0]

# Evaluate the forecast
mse = mean_squared_error(test, forecast)
print('Mean Squared Error:', mse)

# Plot the results
data.plot()
test.plot(label='Actual')
pd.Series(forecast, index=test.index).plot(label='Forecast')
plt.legend()
plt.show()

Here's how this code works:

1. We first load the time series data from a CSV file, assuming the data has a 'date' column that can be parsed as a datetime object.
2. We split the data into training and testing sets, using the last 10 observations as the test set.
3. We fit an ARIMA model on the training data using the `ARIMA` class from the `statsmodels` library. The `order` parameter specifies the ARIMA model parameters (p, d, q).
4. We then use the fitted model to make real-time forecasts for the 10 test set observations using the `forecast()` method.
5. We evaluate the forecast by calculating the mean squared error (MSE) between the actual and predicted values.
6. Finally, we plot the actual and forecasted values to visually inspect the results.

Note that this is a basic example, and in a real-world scenario, you would need to perform more thorough data preprocessing, model selection, and evaluation to ensure accurate real-time forecasts. Additionally, you may want to consider using more advanced forecasting techniques, such as SARIMA, Prophet, or deep learning models, depending on the characteristics of your time series data.