devdunia/prophet.html at main · echobash/devdunia · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <link rel="icon" type="image/png" href="images/logo.png">
    <link rel="apple-touch-icon" href="images/logo.png">
    <title>Facebook Prophet - Developer Toolkit | DevDunia</title>
    <meta name="description" content="Facebook Prophet time series forecasting and trend analysis examples. Free online Prophet reference for developers.">
    <script src="https://cdn.tailwindcss.com"></script>
    <script src="js/common.js"></script>
    <style>
        body {
            background: linear-gradient(135deg, #0f172a 0%, #1e293b 50%, #0f172a 100%);
        }
    </style>
</head>
<body class="h-screen lg:ml-72">
    <!-- Background: Consistent Subtle Dark Gradient -->
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        <div class="relative z-10 container mx-auto px-4 pt-16 pb-24 sm:px-6 lg:px-8">

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            <h1 class="text-3xl sm:text-4xl font-bold mb-4 tracking-tight
                       text-transparent bg-clip-text bg-gradient-to-r from-emerald-400 to-teal-500">
                Facebook Prophet
            </h1>
            <p class="text-lg text-gray-400 max-w-2xl mx-auto">
                Time series forecasting and trend analysis with Facebook Prophet.
            </p>
        </div>

        <!-- Tool Container -->
        <div class="max-w-6xl mx-auto">
            <div class="bg-slate-800/70 backdrop-blur-md rounded-lg shadow-lg border border-slate-700/60 p-6">

                <!-- Setup Section -->
                <div class="mb-8">
                    <h2 class="text-xl font-semibold text-emerald-300 mb-4">Setup & Installation</h2>
                    <div class="bg-slate-700/30 rounded-lg p-4 border border-slate-600/50">
                        <div class="flex items-center justify-between mb-3">
                            <h3 class="text-lg font-medium text-emerald-300">Installation & Basic Setup</h3>
                            <button class="copy-btn px-3 py-1 bg-emerald-600 hover:bg-emerald-700 text-white text-xs rounded transition-colors duration-200 flex items-center space-x-1" data-target="setup-code">
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                                    <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M8 16H6a2 2 0 01-2-2V6a2 2 0 012-2h8a2 2 0 012 2v8a2 2 0 01-2 2h-2m-6-4l2 2m0 0l2-2m-2 2V10"></path>
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                        <textarea
                            id="setup-code"
                                rows="8"
                                class="w-full px-3 py-2 bg-slate-800/50 border border-slate-600 rounded text-gray-200 font-mono text-sm resize-none"
                                readonly
                        ># Install Prophet
pip install prophet

# Install additional dependencies
pip install pandas numpy matplotlib seaborn

# Basic imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from prophet import Prophet
import warnings
warnings.filterwarnings('ignore')

# Set up plotting
plt.style.use('default')
plt.rcParams['figure.figsize'] = (12, 6)</textarea>
                    </div>
                </div>

                <!-- Basic Forecasting Section -->
                <div class="mb-8">
                    <h2 class="text-xl font-semibold text-emerald-300 mb-4">Basic Forecasting</h2>
                    <div class="grid grid-cols-1 md:grid-cols-2 gap-6">

                        <!-- Simple Forecast -->
                        <div class="bg-slate-700/30 rounded-lg p-4 border border-slate-600/50">
                            <div class="flex items-center justify-between mb-3">
                                <h3 class="text-lg font-medium text-emerald-300">Simple Forecast</h3>
                                <button class="copy-btn px-3 py-1 bg-emerald-600 hover:bg-emerald-700 text-white text-xs rounded transition-colors duration-200 flex items-center space-x-1" data-target="simple-forecast-code">
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                                        <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M8 16H6a2 2 0 01-2-2V6a2 2 0 012-2h8a2 2 0 012 2v8a2 2 0 01-2 2h-2m-6-4l2 2m0 0l2-2m-2 2V10"></path>
                                    </svg>
                                    <span>Copy</span>
                                </button>
                            </div>
                            <textarea
                                id="simple-forecast-code"
                                rows="15"
                                class="w-full px-3 py-2 bg-slate-800/50 border border-slate-600 rounded text-gray-200 font-mono text-sm resize-none"
                                readonly
                            ># Create sample data
dates = pd.date_range('2020-01-01', '2023-12-31', freq='D')
values = 100 + np.cumsum(np.random.randn(len(dates)) * 0.5) + 10 * np.sin(np.arange(len(dates)) * 2 * np.pi / 365)

# Create DataFrame
df = pd.DataFrame({
    'ds': dates,
    'y': values
})

# Initialize and fit Prophet model
model = Prophet()
model.fit(df)

# Create future dataframe
future = model.make_future_dataframe(periods=365)  # Forecast 1 year ahead
forecast = model.predict(future)

# Plot the forecast
fig = model.plot(forecast)
plt.title('Prophet Forecast')
plt.show()

# Plot components
fig = model.plot_components(forecast)
plt.show()

# Access forecast data
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())</textarea>
                        </div>

                        <!-- With Seasonality -->
                        <div class="bg-slate-700/30 rounded-lg p-4 border border-slate-600/50">
                            <div class="flex items-center justify-between mb-3">
                                <h3 class="text-lg font-medium text-emerald-300">With Seasonality</h3>
                                <button class="copy-btn px-3 py-1 bg-emerald-600 hover:bg-emerald-700 text-white text-xs rounded transition-colors duration-200 flex items-center space-x-1" data-target="seasonality-code">
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                                    </svg>
                                    <span>Copy</span>
                                </button>
                            </div>
                            <textarea
                                id="seasonality-code"
                                rows="15"
                                class="w-full px-3 py-2 bg-slate-800/50 border border-slate-600 rounded text-gray-200 font-mono text-sm resize-none"
                                readonly
                            ># Create data with strong seasonality
dates = pd.date_range('2020-01-01', '2023-12-31', freq='D')
# Add yearly seasonality
yearly_season = 20 * np.sin(2 * np.pi * np.arange(len(dates)) / 365.25)
# Add weekly seasonality
weekly_season = 5 * np.sin(2 * np.pi * np.arange(len(dates)) / 7)
# Add trend
trend = np.linspace(100, 200, len(dates))
# Add noise
noise = np.random.randn(len(dates)) * 2

values = trend + yearly_season + weekly_season + noise

df = pd.DataFrame({
    'ds': dates,
    'y': values
})

# Initialize Prophet with custom seasonality
model = Prophet(
    yearly_seasonality=True,
    weekly_seasonality=True,
    daily_seasonality=False,
    seasonality_mode='multiplicative'  # or 'additive'
)

# Add custom seasonality
model.add_seasonality(name='monthly', period=30.5, fourier_order=5)

# Fit the model
model.fit(df)

# Make predictions
future = model.make_future_dataframe(periods=365)
forecast = model.predict(future)

# Plot results
fig = model.plot(forecast)
plt.title('Prophet Forecast with Seasonality')
plt.show()

# Plot components
fig = model.plot_components(forecast)
plt.show()</textarea>
                        </div>

                    </div>
                </div>

                <!-- Advanced Features Section -->
                <div class="mb-8">
                    <h2 class="text-xl font-semibold text-emerald-300 mb-4">Advanced Features</h2>
                    <div class="grid grid-cols-1 md:grid-cols-2 gap-6">

                        <!-- Holidays and Events -->
                        <div class="bg-slate-700/30 rounded-lg p-4 border border-slate-600/50">
                            <div class="flex items-center justify-between mb-3">
                                <h3 class="text-lg font-medium text-emerald-300">Holidays and Events</h3>
                                <button class="copy-btn px-3 py-1 bg-emerald-600 hover:bg-emerald-700 text-white text-xs rounded transition-colors duration-200 flex items-center space-x-1" data-target="holidays-code">
                                    <svg class="w-3 h-3" fill="none" stroke="currentColor" viewBox="0 0 24 24">
                                        <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M8 16H6a2 2 0 01-2-2V6a2 2 0 012-2h8a2 2 0 012 2v8a2 2 0 01-2 2h-2m-6-4l2 2m0 0l2-2m-2 2V10"></path>
                                    </svg>
                                    <span>Copy</span>
                                </button>
                            </div>
                            <textarea
                                id="holidays-code"
                                rows="15"
                                class="w-full px-3 py-2 bg-slate-800/50 border border-slate-600 rounded text-gray-200 font-mono text-sm resize-none"
                                readonly
                            ># Create custom holidays
holidays = pd.DataFrame({
    'holiday': ['New Year', 'Christmas', 'Black Friday', 'Summer Sale'],
    'ds': pd.to_datetime(['2020-01-01', '2020-12-25', '2020-11-27', '2020-07-15',
                         '2021-01-01', '2021-12-25', '2021-11-26', '2021-07-15',
                         '2022-01-01', '2022-12-25', '2022-11-25', '2022-07-15']),
    'lower_window': 0,
    'upper_window': 1,
})

# Create sample data with holiday effects
dates = pd.date_range('2020-01-01', '2022-12-31', freq='D')
base_values = 100 + np.cumsum(np.random.randn(len(dates)) * 0.3)

# Add holiday effects
holiday_effects = np.zeros(len(dates))
for _, holiday in holidays.iterrows():
    mask = (dates >= holiday['ds']) & (dates <= holiday['ds'] + pd.Timedelta(days=holiday['upper_window']))
    holiday_effects[mask] += 20  # Boost during holidays

values = base_values + holiday_effects

df = pd.DataFrame({
    'ds': dates,
    'y': values
})

# Initialize Prophet with holidays
model = Prophet(holidays=holidays)
model.fit(df)

# Make predictions
future = model.make_future_dataframe(periods=365)
forecast = model.predict(future)

# Plot results
fig = model.plot(forecast)
plt.title('Prophet Forecast with Holidays')
plt.show()

# Plot components including holidays
fig = model.plot_components(forecast)
plt.show()</textarea>
                        </div>

                        <!-- Regressors -->
                        <div class="bg-slate-700/30 rounded-lg p-4 border border-slate-600/50">
                            <div class="flex items-center justify-between mb-3">
                                <h3 class="text-lg font-medium text-emerald-300">External Regressors</h3>
                                <button class="copy-btn px-3 py-1 bg-emerald-600 hover:bg-emerald-700 text-white text-xs rounded transition-colors duration-200 flex items-center space-x-1" data-target="regressors-code">
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                                    </svg>
                                    <span>Copy</span>
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                            </div>
                            <textarea
                                id="regressors-code"
                                rows="15"
                                class="w-full px-3 py-2 bg-slate-800/50 border border-slate-600 rounded text-gray-200 font-mono text-sm resize-none"
                                readonly
                            ># Create data with external regressors
dates = pd.date_range('2020-01-01', '2023-12-31', freq='D')

# Create external regressors
temperature = 20 + 10 * np.sin(2 * np.pi * np.arange(len(dates)) / 365.25) + np.random.randn(len(dates)) * 2
advertising = np.random.exponential(100, len(dates))
competitor_price = 50 + np.cumsum(np.random.randn(len(dates)) * 0.1)

# Create target variable influenced by regressors
base_sales = 1000
temp_effect = 5 * temperature
ad_effect = 0.1 * advertising
price_effect = -2 * competitor_price
trend = np.linspace(0, 200, len(dates))
noise = np.random.randn(len(dates)) * 50

sales = base_sales + temp_effect + ad_effect + price_effect + trend + noise

# Create DataFrame
df = pd.DataFrame({
    'ds': dates,
    'y': sales,
    'temperature': temperature,
    'advertising': advertising,
    'competitor_price': competitor_price
})

# Initialize Prophet and add regressors
model = Prophet()
model.add_regressor('temperature')
model.add_regressor('advertising')
model.add_regressor('competitor_price')

# Fit the model
model.fit(df)

# Create future dataframe with regressor values
future = model.make_future_dataframe(periods=365)

# For future periods, you need to provide regressor values
# This is a simplified example - in practice, you'd forecast these too
future['temperature'] = np.tile(temperature[-365:], 1)  # Repeat last year
future['advertising'] = np.tile(advertising[-365:], 1)
future['competitor_price'] = np.tile(competitor_price[-365:], 1)

# Make predictions
forecast = model.predict(future)

# Plot results
fig = model.plot(forecast)
plt.title('Prophet Forecast with External Regressors')
plt.show()</textarea>
                        </div>

                    </div>
                </div>

                <!-- Model Evaluation Section -->
                <div class="mb-8">
                    <h2 class="text-xl font-semibold text-emerald-300 mb-4">Model Evaluation</h2>
                    <div class="bg-slate-700/30 rounded-lg p-4 border border-slate-600/50">
                        <div class="flex items-center justify-between mb-3">
                            <h3 class="text-lg font-medium text-emerald-300">Cross-Validation and Metrics</h3>
                            <button class="copy-btn px-3 py-1 bg-emerald-600 hover:bg-emerald-700 text-white text-xs rounded transition-colors duration-200 flex items-center space-x-1" data-target="evaluation-code">
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                                    <path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M8 16H6a2 2 0 01-2-2V6a2 2 0 012-2h8a2 2 0 012 2v8a2 2 0 01-2 2h-2m-6-4l2 2m0 0l2-2m-2 2V10"></path>
                                </svg>
                                <span>Copy</span>
                            </button>
                        </div>
                        <textarea
                            id="evaluation-code"
                            rows="20"
                            class="w-full px-3 py-2 bg-slate-800/50 border border-slate-600 rounded text-gray-200 font-mono text-sm resize-none"
                            readonly
                        >from prophet.diagnostics import cross_validation, performance_metrics
from prophet.plot import plot_cross_validation_metric
import matplotlib.pyplot as plt

# Create sample data
dates = pd.date_range('2020-01-01', '2023-12-31', freq='D')
values = 100 + np.cumsum(np.random.randn(len(dates)) * 0.5) + 10 * np.sin(np.arange(len(dates)) * 2 * np.pi / 365)

df = pd.DataFrame({
    'ds': dates,
    'y': values
})

# Initialize and fit model
model = Prophet()
model.fit(df)

# Cross-validation
df_cv = cross_validation(
    model,
    initial='730 days',  # Use first 2 years for training
    period='180 days',   # Every 6 months
    horizon='90 days',   # Forecast 3 months ahead
    parallel="processes"
)

# Calculate performance metrics
df_performance = performance_metrics(df_cv)
print("Performance Metrics:")
print(df_performance.head())

# Plot cross-validation metrics
fig = plot_cross_validation_metric(df_cv, metric='mape')
plt.title('Mean Absolute Percentage Error')
plt.show()

# Manual evaluation
def evaluate_model(model, df, test_size=0.2):
    # Split data
    split_point = int(len(df) * (1 - test_size))
    train_df = df[:split_point]
    test_df = df[split_point:]

    # Fit model on training data
    model.fit(train_df)

    # Make predictions for test period
    future = model.make_future_dataframe(periods=len(test_df))
    forecast = model.predict(future)

    # Get predictions for test period
    test_forecast = forecast[split_point:].reset_index(drop=True)

    # Calculate metrics
    mae = np.mean(np.abs(test_df['y'] - test_forecast['yhat']))
    mape = np.mean(np.abs((test_df['y'] - test_forecast['yhat']) / test_df['y'])) * 100
    rmse = np.sqrt(np.mean((test_df['y'] - test_forecast['yhat'])**2))

    print(f"MAE: {mae:.2f}")
    print(f"MAPE: {mape:.2f}%")
    print(f"RMSE: {rmse:.2f}")

    return mae, mape, rmse

# Evaluate model
mae, mape, rmse = evaluate_model(model, df)</textarea>
                    </div>
                </div>

                <!-- Quick Reference Section -->
                <div class="mt-8 p-4 bg-slate-700/30 rounded-lg border border-slate-600/50">
                    <h3 class="text-lg font-semibold text-emerald-400 mb-3">Quick Reference</h3>
                    <div class="grid grid-cols-1 md:grid-cols-2 gap-4 text-sm">
                        <div>
                            <p class="text-gray-300 font-medium mb-2">Key Parameters:</p>
                            <ul class="text-gray-400 space-y-1">
                                <li>• <code>yearly_seasonality</code> - Enable yearly patterns</li>
                                <li>• <code>weekly_seasonality</code> - Enable weekly patterns</li>
                                <li>• <code>daily_seasonality</code> - Enable daily patterns</li>
                                <li>• <code>seasonality_mode</code> - 'additive' or 'multiplicative'</li>
                                <li>• <code>changepoint_prior_scale</code> - Trend flexibility</li>
                                <li>• <code>seasonality_prior_scale</code> - Seasonality strength</li>
                            </ul>
                        </div>
                        <div>
                            <p class="text-gray-300 font-medium mb-2">Common Methods:</p>
                            <ul class="text-gray-400 space-y-1">
                                <li>• <code>fit()</code> - Train the model</li>
                                <li>• <code>predict()</code> - Make forecasts</li>
                                <li>• <code>plot()</code> - Plot forecast</li>
                                <li>• <code>plot_components()</code> - Plot components</li>
                                <li>• <code>add_seasonality()</code> - Custom seasonality</li>
                                <li>• <code>add_regressor()</code> - External variables</li>
                            </ul>
                        </div>
                    </div>
                </div>

            </div>
        </div>

        </div>
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