model_air.py

# Drop irrelevant columns
cols_to_drop = ['PT08.S1(CO)', 'C6H6(GT)', 'PT08.S2(NMHC)', 'PT08.S4(NO2)', 'PT08.S5(O3)', 'T', 'RH', 'AH']

weekly_data = weekly_data.drop(cols_to_drop, axis=1)

# Import Prophet
from fbprophet import Prophet
import logging

logging.getLogger().setLevel(logging.ERROR)

# Change the column names according to Prophet's guidelines
df = weekly_data.reset_index()
df.columns = ['ds', 'y']
df.head()

# Split into a train/test set
prediction_size = 30
train_df = df[:-prediction_size]

# Initialize and train a model
m = Prophet()
m.fit(train_df)

# Make predictions
future = m.make_future_dataframe(periods=prediction_size)
forecast = m.predict(future)
forecast.head()

# Plot forecast
m.plot(forecast)

# Plot forecast's components
m.plot_components(forecast)

# Evaluate the model
def make_comparison_dataframe(historical, forecast):
    return forecast.set_index('ds')[['yhat', 'yhat_lower', 'yhat_upper']].join(historical.set_index('ds'))

cmp_df = make_comparison_dataframe(df, forecast)
cmp_df.head()

def calculate_forecast_errors(df, prediction_size):
    
    df = df.copy()
    
    df['e'] = df['y'] - df['yhat']
    df['p'] = 100 * df['e'] / df['y']
    
    predicted_part = df[-prediction_size:]
    
    error_mean = lambda error_name: np.mean(np.abs(predicted_part[error_name]))
    
    return {'MAPE': error_mean('p'), 'MAE': error_mean('e')}

for err_name, err_value in calculate_forecast_errors(cmp_df, prediction_size).items():
    print(err_name, err_value)

# Plot forecast with upper and lower bounds
plt.figure(figsize=(17, 8))
plt.plot(cmp_df['yhat'])
plt.plot(cmp_df['yhat_lower'])
plt.plot(cmp_df['yhat_upper'])
plt.plot(cmp_df['y'])
plt.xlabel('Time')
plt.ylabel('Average Weekly NOx Concentration')
plt.grid(False)
plt.show()