How to forecast call volumes using Facebooks Prophet Model
Let’s start with some basics
- Sign in to Google Collab
- Create a CSV file with your dataset (the best option is to use day level dataset with at least a years worth of data)
- Make sure you name the columns as Date & Volume (otherwise you would need to edit the code a bit in step 4)
- Let’s load your data into Google Collab (use the upload to session icon)
- You are now ready to forecast !
We will use Facebook’s Prophet algorithm to forecast call volumes (To read more about it click here)
- #install prophet forecast module
! pip install prophet - # Load the necessary libraries
from prophet import Prophet
import pandas as pd - # Load the time series data
data = pd.read_csv(“/Forecast.csv”) - # Convert the data to the correct format for prophet – if you have different column names – you can change them here now
data_prophet = data.rename(columns={‘Date’: ‘ds’, ‘Volume’: ‘y’}) - # Create the prophet model
m = Prophet(changepoint_prior_scale=0.5, seasonality_mode=’multiplicative’) - # Add seasonality to the prophet model
m.add_seasonality(name=’weekly’, period=7, fourier_order=5) - # Fit the model to the data
m.fit(data_prophet) - # Create a dataframe for future predictions – we create a forecast for next 1 year
future = m.make_future_dataframe(periods=365)
# Generate the forecast
forecast = m.predict(future) - # Plot the forecast
fig = m.plot(forecast)
fig2 = m.plot_components(forecast) - # Print the forecast data
print(forecast)
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