> ## Documentation Index
> Fetch the complete documentation index at: https://nixtla.io/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Forecasting Web Traffic

> Learn how to predict website traffic patterns using TimeGPT.

> [!NOTE]
>
> **Goal:** Forecast the next 7 days of daily visits to the website [cienciadedatos.net](https://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:

> [card] Improve Accuracy
>
> Obtain forecasts nearly 10% more accurate than the original method.

> [card] Reduce Complexity
>
> Use significantly fewer lines of code and simpler workflows.

> [card] Save Time
>
> Generate forecasts in substantially less computation time.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/use-cases/1_forecasting_web_traffic.ipynb)

**1. 1. Import Packages and Initialize Client**

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

```python Nixtla Client Initialization theme={null}
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")
)
```

> [!TIP]
>
> **Use an Azure AI endpoint**
>
> If you are using an Azure AI endpoint, also set the `base_url` argument:
>
> ```python Azure AI Endpoint Setup theme={null}
> 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`.

```python Load and Format Data theme={null}
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)
```

> [accordion] 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]
>
> **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.

```python Cross-validation Setup theme={null}
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()
```

![CV Plot](https://raw.githubusercontent.com/Nixtla/nixtla/readme_docs/nbs/_docs/docs/use-cases/1_forecasting_web_traffic_files/figure-markdown_strict/cell-12-output-1.png)

Cross-validation forecast plot

The results align closely with those from the original tutorial on [forecasting web traffic with machine learning](https://cienciadedatos.net/documentos/py37-forecasting-web-traffic-machine-learning.html).

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

```python Calculate MAE theme={null}
from utilsforecast.losses import mae

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

mae_timegpt
```

> [!NOTE]
>
> **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.

```python Add Weekday Indicators theme={null}
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:

```python Cross-validation with Exogenous Variables theme={null}
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]
)
```

![Exogenous CV Plot](https://raw.githubusercontent.com/Nixtla/nixtla/readme_docs/nbs/_docs/docs/use-cases/1_forecasting_web_traffic_files/figure-markdown_strict/cell-17-output-1.png)

Forecast with Exogenous Variables

> [!NOTE]
>
> Adding weekday indicators can capture weekly seasonality in user visits.

**5. 5. Comparing Results**

> [card] 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**

> [!TIP]
>
> **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!**