demo_prophet.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from prophet import Prophet
import logging


# kW
PREDICTION_LOWER_BOUND = 0 # 15
print("do not forget about hardwired prediction lower bound", PREDICTION_LOWER_BOUND, "kW")


def prediction_task(df, split_date, forecast_horizon):
    # Split the data into training (past) and evaluation (future) sets
    train_data = df[df['ds'] <= split_date]
    eval_data = df[df['ds'] > split_date]


    # Initialize and train the Prophet model using the training data
    model = Prophet(seasonality_mode='multiplicative', growth='flat',
        yearly_seasonality=False, weekly_seasonality=True, daily_seasonality=True)

    model.fit(train_data)

    # Create a DataFrame with future timestamps for the evaluation period
    future = model.make_future_dataframe(periods=forecast_horizon, freq='15T', include_history=False)

    # Make predictions for the evaluation period
    forecast = model.predict(future)

    # Calculate evaluation metrics (e.g., MAE, MSE, RMSE) by comparing eval_predictions with eval_data['y']

    # For example, you can calculate MAE as follows:
    from sklearn.metrics import mean_absolute_error

    eval_data = eval_data[eval_data['ds'] <= forecast['ds'].max()]
    for key in ('yhat', 'yhat_lower', 'yhat_upper'):
        forecast[key] = np.maximum(forecast[key], PREDICTION_LOWER_BOUND)

    mae = mean_absolute_error(eval_data['y'], forecast['yhat'])

    # Print or store the evaluation metrics

    do_vis = False
    if do_vis:
        plt.figure(figsize=(12, 6))
        plt.plot(eval_data['ds'], eval_data['y'], label='Actual', color='blue')
        plt.plot(forecast['ds'], forecast['yhat'], label='Predicted', color='red')
        plt.fill_between(forecast['ds'], forecast['yhat_lower'], forecast['yhat_upper'], color='pink', alpha=0.5, label='Uncertainty')
        plt.xlabel('Timestamp')
        plt.ylabel('Value')
        plt.title('Actual vs. Predicted Values')
        plt.legend()
        plt.grid(True)
        plt.show()
        '''
        fig1 = model.plot(forecast)
        plt.plot(eval_data['ds'], eval_data['y'], c='r')
        plt.show()
        '''

        fig2 = model.plot_components(forecast)
        plt.show()
    return mae, eval_data['y'].mean()


logger = logging.getLogger('cmdstanpy')
logger.addHandler(logging.NullHandler())
logger.propagate = False
logger.setLevel(logging.CRITICAL)



cons_filename = 'pq_terheles_2021_adatok.tsv'

df = pd.read_csv(cons_filename, sep='\t', skipinitialspace=True, na_values='n/a', decimal=',')
df['Time'] = pd.to_datetime(df['Korrigált időpont'], format='%m/%d/%y %H:%M')
df = df.set_index('Time')
df['Consumption'] = df['Hatásos teljesítmény [kW]']

df['ds'] = df.index
df['y'] = df['Consumption']


# TODO 15 minutes timestep hardwired!
forecast_horizon = 7 * 24 * 4


start_date = '2021-06-01'
end_date = '2021-10-24'

weekly_date_range = pd.date_range(start=start_date, end=end_date, freq='8d')


maes = []
mean_values = []
for split_date in weekly_date_range:
    mae, mean_value = prediction_task(df, split_date, forecast_horizon)
    maes.append(mae)
    mean_values.append(mean_value)
    print(split_date, "Mean Absolute Error", mae, "MAE/true mean", mae / mean_value)

maes = np.array(maes)
mean_values = np.array(mean_values)
aggregate_mae = maes.mean()
print("Mean Absolute Error over whole date range", weekly_date_range[0], "-", weekly_date_range[-1], ":", aggregate_mae)
print("Mean Absolute Error / true mean over whole date range", aggregate_mae / mean_values.mean())