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docs: updated to include fill_gaps capability for multiple time series #580

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docs: updated to include fill_gaps capability for multiple time series #580

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@ngupta23 ngupta23 commented Dec 19, 2024 •
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github-actions bot commented Dec 19, 2024 •
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Experiment Results

Experiment 1: air-passengers

Description:

variable experiment
h 12
season_length 12
freq MS
level None
n_windows 1

Results:

metric timegpt-1 timegpt-1-long-horizon SeasonalNaive Naive
mae 12.6793 11.0623 47.8333 76
mape 0.027 0.0232 0.0999 0.1425
mse 213.936 199.132 2571.33 10604.2
total_time 0.5695 0.4785 0.0042 0.0032

Plot:

Experiment 2: air-passengers

Description:

variable experiment
h 24
season_length 12
freq MS
level None
n_windows 1

Results:

metric timegpt-1 timegpt-1-long-horizon SeasonalNaive Naive
mae 58.1031 58.4587 71.25 115.25
mape 0.1257 0.1267 0.1552 0.2358
mse 4040.21 4110.79 5928.17 18859.2
total_time 0.4904 0.4367 0.0036 0.0035

Plot:

Experiment 3: electricity-multiple-series

Description:

variable experiment
h 24
season_length 24
freq H
level None
n_windows 1

Results:

metric timegpt-1 timegpt-1-long-horizon SeasonalNaive Naive
mae 178.293 268.13 269.23 1331.02
mape 0.0234 0.0311 0.0304 0.1692
mse 121589 219485 213677 4.68961e+06
total_time 0.4946 0.4459 0.0047 0.0042

Plot:

Experiment 4: electricity-multiple-series

Description:

variable experiment
h 168
season_length 24
freq H
level None
n_windows 1

Results:

metric timegpt-1 timegpt-1-long-horizon SeasonalNaive Naive
mae 465.497 346.972 398.956 1119.26
mape 0.062 0.0436 0.0512 0.1583
mse 835021 403760 656723 3.17316e+06
total_time 0.543 0.6118 0.0048 0.0042

Plot:

Experiment 5: electricity-multiple-series

Description:

variable experiment
h 336
season_length 24
freq H
level None
n_windows 1

Results:

metric timegpt-1 timegpt-1-long-horizon SeasonalNaive Naive
mae 558.673 459.757 602.926 1340.95
mape 0.0697 0.0565 0.0787 0.17
mse 1.22723e+06 739114 1.61572e+06 6.04619e+06
total_time 0.7049 0.9043 0.0049 0.0044

Plot:

@jmoralez
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I'm not a fan of pasting docstrings in a notebook, I'd prefer if you linked to the function reference or even just have fill_gaps? in a cell.

@ngupta23
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I'm not a fan of pasting docstrings in a notebook, I'd prefer if you linked to the function reference or even just have fill_gaps? in a cell.

OK, I have updated this using the show_doc capability. I think it is a little richer compared to the ? approach.

image

jmoralez
jmoralez reviewed Dec 20, 2024
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settings.ini Outdated
@@ -16,7 +16,7 @@ custom_sidebar = True
license = apache2
status = 4
requirements = annotated-types httpx[zstd] orjson pandas pydantic>=1.10 tenacity tqdm utilsforecast>=0.2.8
dev_requirements = black datasetsforecast fire hierarchicalforecast jupyterlab nbdev neuralforecast numpy<2 plotly polars pre-commit pyreadr python-dotenv pyyaml setuptools<70 statsforecast tabulate
dev_requirements = black datasetsforecast fire hierarchicalforecast jupyterlab nbdev neuralforecast numpy<2 plotly polars pre-commit pyreadr python-dotenv pyyaml setuptools<70 statsforecast tabulate ipywidgets
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What is this for?

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It is needed to use the show_docs capability provided by nbdev.

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I don't think so, uv run --with nbdev --with utilsforecast python -c 'from nbdev import show_doc; from utilsforecast.preprocessing import fill_gaps; print(show_doc(fill_gaps))' runs fine without it.

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It runs but we get this message.

image

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that seems to come from nixtla, rather than nbdev. Let's leave it but please note that these are sorted alphabetically, so please set it in the right place.

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Done

@ngupta23 ngupta23 requested a review from jmoralez December 20, 2024 18:09
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@elephaint elephaint Awaiting requested review from elephaint

@jmoralez jmoralez Awaiting requested review from jmoralez

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[docs] Forecasting with multiple time series with missing values
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@ngupta23 @jmoralez