fetch_tourism_quarterly¶
yohou.datasets.fetch_tourism_quarterly(*, n_series=None, data_home=None, download_if_missing=True, n_retries=3, delay=1.0)
¶
Fetch the Tourism Quarterly dataset from Monash/Zenodo.
427 quarterly tourism time series from the tourism forecasting competition.
Parameters ¶
| Name | Type | Description | Default |
|---|---|---|---|
n_series
|
int or None
|
Maximum number of series to include. |
None
|
data_home
|
str, PathLike, or None
|
Specify another download and cache folder for the datasets.
By default all yohou data is stored in |
None
|
download_if_missing
|
bool
|
If |
True
|
n_retries
|
int
|
Number of retries when HTTP errors are encountered. |
3
|
delay
|
float
|
Number of seconds between retries. |
1.0
|
Returns ¶
| Type | Description |
|---|---|
Bunch
|
Dictionary-like object with the following attributes: frame : pl.DataFrame
DataFrame with |
See Also ¶
fetch_tourism_monthly: Monthly tourism series from the same competition.fetch_hospital: Monthly hospital patient count series.get_data_home: Return the path of the data directory.
References ¶
- Godahewa, R., Bergmeir, C., Webb, G. I., Hyndman, R. J., & Montero-Manso, P. (2021). "Monash Time Series Forecasting Archive." Neural Information Processing Systems Track on Datasets and Benchmarks. https://doi.org/10.5281/zenodo.4656093
Examples ¶
>>> from yohou.datasets import fetch_tourism_quarterly
>>> bunch = fetch_tourism_quarterly()
>>> bunch.frame.columns[:2]
['time', 'T1__tourists']
Source Code ¶
Source code in src/yohou/datasets/_fetchers.py
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Tutorials¶
The following example notebooks use this component:
-
How to Apply Stationarity to Panel Data
Apply per-group stationarity transforms on panel data with SeasonalDifferencing, DecompositionPipeline (polynomial trend + pattern seasonality), and residuals.
-
How to Choose a Decomposition Strategy
Build 2- and 3-component DecompositionPipeline forecasters chaining trend, seasonality, and residual models with target pre-transformation.
-
How to Configure LocalPanelForecaster
Wrap any forecaster with LocalPanelForecaster for fully independent per-group clones, parallel fitting via n_jobs, and selective group operations.
-
How to Run Panel Cross-Validation
Time series cross-validation on panel data with GridSearchCV, selective group observation, rewind operations, and groupwise performance comparison.