Forecasting Web Traffic

Learn how to predict website traffic patterns using TimeGPT.

Goal: Forecast the next 7 days of daily visits to the website cienciadedatos.net using TimeGPT.

This tutorial is adapted from “Forecasting web traffic with machine learning and Python” by Joaquín Amat Rodrigo and Javier Escobar Ortiz. You will learn how to:

Improve Accuracy

Obtain forecasts nearly 10% more accurate than the original method.

Reduce Complexity

Use significantly fewer lines of code and simpler workflows.

Save Time

Generate forecasts in substantially less computation time.

1. 1. Import Packages and Initialize Client

   To start, import the required packages and initialize the Nixtla client with your API key.

   Nixtla Client Initialization

   import pandas as pd
   from nixtla import NixtlaClient
   
   nixtla_client = NixtlaClient(
       api_key='my_api_key_provided_by_nixtla'  # Defaults to os.environ.get("NIXTLA_API_KEY")
   )
   

   Use an Azure AI endpoint

   
   

   If you are using an Azure AI endpoint, also set the base_url argument:

   Azure AI Endpoint Setup

   nixtla_client = NixtlaClient(
     base_url="your_azure_ai_endpoint",
     api_key="your_api_key"
   )
   

2. 2. Load Data

   We will load the website visit data directly from a CSV file. Then, we format the dataset by adding an identifier column named daily_visits.

   Load and Format Data

   url = (
       'https://raw.githubusercontent.com/JoaquinAmatRodrigo/Estadistica-machine-learning-python/'
       'master/data/visitas_por_dia_web_cienciadedatos.csv'
   )
   df = pd.read_csv(url, sep=',', parse_dates=[0], date_format='%d/%m/%y')
   df['unique_id'] = 'daily_visits'
   
   df.head(10)
   

   Data Preview (first 10 rows)

   	date	users	unique_id
   0	2020-07-01	2324	daily_visits
   1	2020-07-02	2201	daily_visits
   2	2020-07-03	2146	daily_visits
   3	2020-07-04	1666	daily_visits
   4	2020-07-05	1433	daily_visits
   5	2020-07-06	2195	daily_visits
   6	2020-07-07	2240	daily_visits
   7	2020-07-08	2295	daily_visits
   8	2020-07-09	2279	daily_visits
   9	2020-07-10	2155	daily_visits

   Note: No further preprocessing is required before we start forecasting.

3. 3. Cross-Validation with TimeGPT

   We will set up a rolling window cross-validation using TimeGPT. This will help us evaluate the forecast accuracy across multiple historic windows.

   Cross-validation Setup

   timegpt_cv_df = nixtla_client.cross_validation(
       df,
       h=7,
       n_windows=8,
       time_col='date',
       target_col='users',
       freq='D',
       level=[80, 90, 99.5]
   )
   
   timegpt_cv_df.head()
   

   

   Cross-validation forecast plot

   The results align closely with those from the original tutorial on forecasting web traffic with machine learning.

   Next, we compute the Mean Absolute Error (MAE) to quantify forecast accuracy:

   Calculate MAE

   from utilsforecast.losses import mae
   
   mae_timegpt = mae(
       df=timegpt_cv_df.drop(columns=['cutoff']),
       models=['TimeGPT'],
       target_col='users'
   )
   
   mae_timegpt
   

   MAE Result: The MAE obtained is 167.69, outperforming the original pipeline.

4. 4. Adding Exogenous Variables (Weekday Indicators)

   Exogenous variables can provide additional context that may improve forecast accuracy. In this example, we add binary indicators for each day of the week.

   Add Weekday Indicators

   for i in range(7):
       df[f'week_day_{i + 1}'] = 1 * (df['date'].dt.weekday == i)
   
   df.head(10)
   

   We repeat the cross-validation with these new features:

   Cross-validation with Exogenous Variables

   timegpt_cv_df_with_ex = nixtla_client.cross_validation(
       df,
       h=7,
       n_windows=8,
       time_col='date',
       target_col='users',
       freq='D',
       level=[80, 90, 99.5]
   )
   

   

   Forecast with Exogenous Variables

   Adding weekday indicators can capture weekly seasonality in user visits.

5. 5. Comparing Results

   Results

   Model	Exogenous features	MAE Backtest
   TimeGPT	No	167.6917
   TimeGPT	Yes	167.2286

   We see a slight improvement in MAE by including the weekday indicators. This illustrates how TimeGPT can incorporate additional signals without complex data processing or extensive model tuning.

6. 6. Final Thoughts

   Key Takeaways

   • TimeGPT simplifies forecasting workflows by reducing code and tuning overhead.

   • Feature engineering (like adding weekday variables) further boosts accuracy.

   • Cross-validation provides a robust way to evaluate model performance.

   We have demonstrated significant improvements in forecasting accuracy with minimal effort using TimeGPT. This avoids the majority of the complex steps required when building custom models—such as extensive feature engineering, validation, model comparisons, and hyperparameter tuning.

   Good luck and happy forecasting!