Create a list of dictionaries with the past usage data
data = [
    {'year': 2018, 'number_usage': 235446},
    {'year': 2019, 'number_usage': 288847},
    {'year': 2020, 'number_usage': 194249},
    {'year': 2021, 'number_usage': 205100},
    {'year': 2022, 'number_usage': 401755},
    {'year': 2023, 'number_usage': 17841219}
]
 
# Create a DataFrame with the past usage data
df = pd.DataFrame(data)
 
# Train an ARIMA model
model = ARIMA(df['number_usage'], order=(1, 1, 1))
model_fit = model.fit()
 
# Make predictions for future years
future_years = [2024, 2025, 2026]
future_predictions = model_fit.forecast(len(future_years))[0]
 
# Create a DataFrame with the predicted future usage data
future_data = [
    {'year': 2024, 'predicted_number_usage': future_predictions[0]},
    {'year': 2025, 'predicted_number_usage': future_predictions[1]},
    {'year': 2026, 'predicted_number_usage': future_predictions[2]}
]
future_df = pd.DataFrame(future_data)
 
print(future_df)

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