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[DOC] - Add links and callouts #355

Merged
merged 3 commits into from
May 16, 2024
Merged

[DOC] - Add links and callouts #355

merged 3 commits into from
May 16, 2024

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marcopeix
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Add links in page "About TimeGPT"
Add links in sections of capabilities. The links go to the notebooks in capabilities.
Add callouts for TimeGPT on Azure in all capabilities notebooks and tutorial 01 to 08.

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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 1.7844 4.2574 0.008 0.0045

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 2.4971 2.1842 0.0052 0.0046

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 178.293 196.363 269.23 1331.02
mape 0.0234 0.0234 0.0304 0.1692
mse 121588 123119 213677 4.68961e+06
total_time 1.9209 3.5239 0.0075 0.0064

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 465.532 353.528 398.956 1119.26
mape 0.062 0.0454 0.0512 0.1583
mse 835120 422332 656723 3.17316e+06
total_time 3.9713 2.558 0.0071 0.0065

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 558.649 361.033 602.926 1340.95
mape 0.0697 0.046 0.0787 0.17
mse 1.22721e+06 441118 1.61572e+06 6.04619e+06
total_time 4.1055 2.4983 0.007 0.0067

Plot:

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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 213.935 199.132 2571.33 10604.2
total_time 2.4419 1.9871 0.0081 0.0042

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 4040.22 4110.79 5928.17 18859.2
total_time 3.8599 4.5664 0.0054 0.0044

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 178.293 196.363 269.23 1331.02
mape 0.0234 0.0234 0.0304 0.1692
mse 121588 123119 213677 4.68961e+06
total_time 2.2115 2.6671 0.0074 0.0065

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 465.532 353.528 398.956 1119.26
mape 0.062 0.0454 0.0512 0.1583
mse 835120 422332 656723 3.17316e+06
total_time 10.1462 5.1483 0.0069 0.0063

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 558.649 361.033 602.926 1340.95
mape 0.0697 0.046 0.0787 0.17
mse 1.22721e+06 441118 1.61572e+06 6.04619e+06
total_time 6.9116 5.2897 0.007 0.0065

Plot:

@marcopeix marcopeix marked this pull request as ready for review May 14, 2024 19:16
AzulGarza
AzulGarza requested changes May 14, 2024
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@AzulGarza AzulGarza left a comment •
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thanks @marcopeix! i deployed the readme dot com version from this branch.

could we fix the following?

  1. the lists of the sections rendered this way:
image

i think we can use multiple breaklines as in the previous docs:


* [Simple anomaly detection](https://docs.nixtla.io/docs/capabilities-anomaly-detection-quickstart)

* [Anomaly detection with exogenous features](https://docs.nixtla.io/docs/capabilities-anomaly-detection-add_exogenous_variables)

...
  1. also, when instantiating the NixtlaClass, we have the following:
image

i think this one can be solved also adding ticks:

 `nixtla_client = NixtlaClient(`
 `    base_url="you azure ai endpoint",`
 `   api_key="your api_key",`
`)`

the last one looks pretty cool!

image

@AzulGarza
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hey @marcopeix! just reviewed #357, and saw that @elephaint used the readme dot com callout style, and i think it looks very cool, wdyt if we use that? please see #357 (review).

image

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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 1.83 3.3279 0.0085 0.0046

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 1.9654 1.9466 0.0055 0.0046

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 178.293 268.121 269.23 1331.02
mape 0.0234 0.0311 0.0304 0.1692
mse 121588 219457 213677 4.68961e+06
total_time 3.255 2.1337 0.0072 0.0065

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 465.532 346.984 398.956 1119.26
mape 0.062 0.0437 0.0512 0.1583
mse 835120 403787 656723 3.17316e+06
total_time 3.7982 3.9673 0.0073 0.0068

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 558.649 459.769 602.926 1340.95
mape 0.0697 0.0566 0.0787 0.17
mse 1.22721e+06 739135 1.61572e+06 6.04619e+06
total_time 7.3833 3.4668 0.0072 0.0067

Plot:

@marcopeix
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Fixed layout issues and used the readme.com style for callouts

@marcopeix marcopeix requested a review from AzulGarza May 15, 2024 18:52
AzulGarza
AzulGarza approved these changes May 16, 2024
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thank you @marcopeix! lgtm

@AzulGarza AzulGarza merged commit 2895af8 into main May 16, 2024
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@AzulGarza AzulGarza deleted the hotfix/docu-marco branch May 16, 2024 03:05
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@AzulGarza AzulGarza AzulGarza approved these changes

@mergenthaler mergenthaler Awaiting requested review from mergenthaler

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