# Implementeing Prophet Technique for Time Series Forecasting using Python

**Anthony Abidakun**

- Published on

### Prophet

Prophet is an open-source library developed by Facebook for time series forecasting. It provides a simplified and automated approach to generate high-quality forecasts. Here's how to use Prophet:

#### Introduction to Prophet

I introduce the key concepts of Prophet, including trend estimation, seasonality modeling, and holiday effects. Prophet can handle various types of time series patterns, including multiple seasonality and non-linear trends.

#### Model Fitting and Forecasting

I fit the Prophet model to the historical time series data and generate forecasts for a specified time

##### Here's a step-by-step guide on implementing Prophet for time series forecasting using Python:

###### Step 1: Install the necessary libraries

Start by installing the required libraries. You can install Prophet using pip:

```
pip install prophet
```

###### Step 2: Import the necessary libraries

Import the required libraries: `pandas`

for data manipulation and `Prophet`

for implementing the Prophet forecasting model.

```
import pandas as pd
from prophet import Prophet
```

###### Step 3: Load and preprocess the time series data

Load your time series data into a pandas DataFrame and preprocess it if necessary. Ensure that the data is in a suitable format with a datetime column named 'ds' and a target column named 'y'.

```
# Load the time series data into a DataFrame
data = pd.read_csv('your_data_file.csv')
# Preprocess the data if necessary (e.g., convert columns to datetime)
data['ds'] = pd.to_datetime(data['ds'])
```

###### Step 4: Create and fit the Prophet model

Create a Prophet model object and fit it to the data.

```
# Create a Prophet model object
model = Prophet()
# Fit the model to the data
model.fit(data)
```

###### Step 5: Generate future dates for forecasting

Generate a DataFrame with future dates for which you want to make predictions.

```
# Create a DataFrame with future dates
future_dates = model.make_future_dataframe(periods=365) # Adjust the number of future dates as needed
```

###### Step 6: Make predictions

Use the fitted Prophet model to make predictions for the future dates.

```
# Make predictions for the future dates
predictions = model.predict(future_dates)
```

###### Step 7: Visualize the results

Plot the actual values and the predicted values to visualize the forecasted time series.

```
import matplotlib.pyplot as plt
# Plot actual values and predicted values
model.plot(predictions)
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('Prophet Forecast')
plt.show()
```

###### Step 8: Access forecasted values and components

You can access various components of the forecasted data, such as trend, seasonality, and uncertainty intervals.

```
# Access the forecasted values
forecasted_values = predictions[['ds', 'yhat']]
# Access the trend component
trend = predictions[['ds', 'trend']]
# Access the seasonality components
seasonality = predictions[['ds', 'seasonality', 'seasonality_lower', 'seasonality_upper']]
# Access the uncertainty intervals
uncertainty = predictions[['ds', 'yhat_lower', 'yhat_upper']]
```

That's it! You have successfully implemented Prophet for time series forecasting using Python. Remember to adjust the code based on your specific dataset and requirements. Additionally, you can explore further by tuning the model parameters, incorporating additional regressors, or performing model diagnostics and evaluation.