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Hotfix/docs v030 #282

Merged
merged 12 commits into from
Apr 16, 2024
Merged

Hotfix/docs v030 #282

merged 12 commits into from
Apr 16, 2024

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MMenchero
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Updated the following docs with the new NixtlaClient class and methods:

  • FAQs
  • Setting Up Your Authentication API Key

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See visual diffs & provide feedback on Jupyter Notebooks.


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github-actions bot commented Apr 4, 2024

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 4.2761 3.323 0.009 0.0055

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 3.5254 13.3165 0.0061 0.0053

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 142.394 196.363 269.23 1331.02
mape 0.0203 0.0234 0.0304 0.1692
mse 63464.7 123119 213677 4.68961e+06
total_time 8.6328 6.8457 0.0085 0.0076

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 522.427 353.528 398.956 1119.26
mape 0.069 0.0454 0.0512 0.1583
mse 966294 422332 656723 3.17316e+06
total_time 6.8067 6.0759 0.0082 0.0078

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 478.362 361.033 602.926 1340.95
mape 0.0622 0.046 0.0787 0.17
mse 805038 441118 1.61572e+06 6.04619e+06
total_time 9.0071 4.226 0.0082 0.0076

Plot:

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github-actions bot commented Apr 4, 2024

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 3.5164 3.4254 0.0093 0.0056

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 17.5753 5.0933 0.0062 0.0055

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 142.394 196.363 269.23 1331.02
mape 0.0203 0.0234 0.0304 0.1692
mse 63464.8 123119 213677 4.68961e+06
total_time 6.166 7.8926 0.0085 0.0076

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 522.427 353.528 398.956 1119.26
mape 0.069 0.0454 0.0512 0.1583
mse 966294 422332 656723 3.17316e+06
total_time 8.2013 7.9555 0.0081 0.0075

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 478.362 361.033 602.926 1340.95
mape 0.0622 0.046 0.0787 0.17
mse 805038 441118 1.61572e+06 6.04619e+06
total_time 9.3351 5.2571 0.0084 0.0079

Plot:

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github-actions bot commented Apr 4, 2024

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 2.8169 3.1768 0.0094 0.0072

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 3.7326 3.2595 0.0061 0.0053

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 142.394 196.363 269.23 1331.02
mape 0.0203 0.0234 0.0304 0.1692
mse 63464.7 123119 213677 4.68961e+06
total_time 4.1581 4.3344 0.0084 0.0076

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 522.427 353.528 398.956 1119.26
mape 0.069 0.0454 0.0512 0.1583
mse 966294 422332 656723 3.17316e+06
total_time 7.378 6.1203 0.0081 0.0077

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 478.362 361.033 602.926 1340.95
mape 0.0622 0.046 0.0787 0.17
mse 805038 441118 1.61572e+06 6.04619e+06
total_time 7.7068 4.3795 0.0082 0.0077

Plot:

Copy link
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github-actions bot commented Apr 4, 2024

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 3.3745 8.3336 0.0092 0.0055

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 4.6583 6.0393 0.0062 0.0053

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 142.394 196.363 269.23 1331.02
mape 0.0203 0.0234 0.0304 0.1692
mse 63464.8 123119 213677 4.68961e+06
total_time 6.9896 6.251 0.0083 0.0074

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 522.427 353.528 398.956 1119.26
mape 0.069 0.0454 0.0512 0.1583
mse 966295 422332 656723 3.17316e+06
total_time 7.8655 5.1167 0.0085 0.0079

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 478.362 361.033 602.926 1340.95
mape 0.0622 0.046 0.0787 0.17
mse 805038 441118 1.61572e+06 6.04619e+06
total_time 20.3564 9.7393 0.0083 0.0078

Plot:

Copy link
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github-actions bot commented Apr 4, 2024

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 5.2562 4.7888 0.0096 0.0054

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 10.9507 7.057 0.0062 0.0054

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 142.394 196.363 269.23 1331.02
mape 0.0203 0.0234 0.0304 0.1692
mse 63464.8 123119 213677 4.68961e+06
total_time 4.8328 7.4005 0.0087 0.0079

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 522.427 353.528 398.956 1119.26
mape 0.069 0.0454 0.0512 0.1583
mse 966294 422332 656723 3.17316e+06
total_time 6.7637 4.504 0.0085 0.0081

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 478.362 361.033 602.926 1340.95
mape 0.0622 0.046 0.0787 0.17
mse 805039 441118 1.61572e+06 6.04619e+06
total_time 10.2693 6.1459 0.0084 0.008

Plot:

@@ -302,15 +302,15 @@
"metadata": {},
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@AzulGarza AzulGarza Apr 8, 2024

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hiere is an error, i think just rerun it might solve it. s


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@@ -35,7 +35,7 @@
"name": "stderr",
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we should use api_key instead of token.


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@@ -15,7 +15,7 @@
"source": [
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@AzulGarza AzulGarza Apr 8, 2024

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the new ulr in docs.nixtla.io/docs will be setting_up_your_authentication_api_key.


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@@ -15,7 +15,7 @@
"source": [
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@AzulGarza AzulGarza Apr 8, 2024

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should we remove this callout warning?

::: {.callout-warning}  Don't confuse the input, output, and finetune tokens with the TimeGPT token or API Key. The input, output, and finetune tokens are used to calculate your usage costs, while the TimeGPT token is used to authenticate your requests.  :::


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I'm actually surprised to see this callout because I know I deleted it since it's no longer necessary. I'll check if there was a problem with version control. Thanks for pointing it out!

@@ -15,7 +15,7 @@
"source": [
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This error occurs when your TimeGPT api_key.


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@@ -36,16 +36,34 @@
"cell_type": "code",
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thanks for adding this.


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@@ -95,7 +95,7 @@
"outputs": [],
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could we check what are the values of weights_x to see if we are returning zeros?


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yes, the weights_x returns zeros.

@@ -95,7 +95,7 @@
"outputs": [],
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see previous comment


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here weights_x also returns zeros.

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AzulGarza commented on 2024-04-08T01:49:57Z
----------------------------------------------------------------

The cross_validation method within the NixtlaClient class


@AzulGarza AzulGarza self-requested a review April 8, 2024 01:50
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awesome work @MMenchero @marcopeix! 🎉

i left a couple of comments. thank you!

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github-actions bot commented Apr 9, 2024

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 14.0421 37.3849 0.0096 0.0055

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 3.2856 4.4936 0.0063 0.0055

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 142.394 196.363 269.23 1331.02
mape 0.0203 0.0234 0.0304 0.1692
mse 63464.8 123119 213677 4.68961e+06
total_time 4.7654 7.6853 0.0085 0.0076

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 522.427 353.528 398.956 1119.26
mape 0.069 0.0454 0.0512 0.1583
mse 966295 422332 656723 3.17316e+06
total_time 9.3922 8.21 0.0085 0.0081

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 478.362 361.033 602.926 1340.95
mape 0.0622 0.046 0.0787 0.17
mse 805038 441118 1.61572e+06 6.04619e+06
total_time 10.6705 7.1599 0.0086 0.0081

Plot:

@AzulGarza AzulGarza self-requested a review April 10, 2024 05:58
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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 4.7285 6.8889 0.0096 0.0054

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 15.1457 4.2142 0.0062 0.0054

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 142.394 196.363 269.23 1331.02
mape 0.0203 0.0234 0.0304 0.1692
mse 63464.7 123119 213677 4.68961e+06
total_time 6.1231 8.5862 0.0086 0.0076

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 522.427 353.528 398.956 1119.26
mape 0.069 0.0454 0.0512 0.1583
mse 966294 422332 656723 3.17316e+06
total_time 6.1579 4.3965 0.0084 0.0078

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 478.362 361.033 602.926 1340.95
mape 0.0622 0.046 0.0787 0.17
mse 805039 441118 1.61572e+06 6.04619e+06
total_time 6.0678 4.0184 0.0082 0.0076

Plot:

Copy link
Contributor

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 4.8835 9.8487 0.0106 0.0057

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 13.3717 7.1074 0.0068 0.0058

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 142.394 196.363 269.23 1331.02
mape 0.0203 0.0234 0.0304 0.1692
mse 63464.7 123119 213677 4.68961e+06
total_time 7.1297 6.646 0.0089 0.0079

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 522.427 353.528 398.956 1119.26
mape 0.069 0.0454 0.0512 0.1583
mse 966295 422332 656723 3.17316e+06
total_time 7.2343 3.5385 0.0086 0.008

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 478.362 361.033 602.926 1340.95
mape 0.0622 0.046 0.0787 0.17
mse 805038 441118 1.61572e+06 6.04619e+06
total_time 10.0472 4.7218 0.0086 0.0079

Plot:

Copy link
Contributor

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 2.8468 2.707 0.0095 0.0054

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 4.0662 2.6699 0.0063 0.0054

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 142.394 196.363 269.23 1331.02
mape 0.0203 0.0234 0.0304 0.1692
mse 63464.7 123119 213677 4.68961e+06
total_time 6.2819 5.0207 0.0086 0.0076

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 522.427 353.528 398.956 1119.26
mape 0.069 0.0454 0.0512 0.1583
mse 966294 422332 656723 3.17316e+06
total_time 4.97 4.2504 0.0093 0.0084

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 478.362 361.033 602.926 1340.95
mape 0.0622 0.046 0.0787 0.17
mse 805039 441118 1.61572e+06 6.04619e+06
total_time 5.7785 5.1292 0.0083 0.0077

Plot:

@AzulGarza AzulGarza merged commit 06b1047 into main Apr 16, 2024
13 checks passed
@AzulGarza AzulGarza deleted the hotfix/docs-v030 branch April 16, 2024 00:25
MMenchero added a commit that referenced this pull request Apr 23, 2024
Co-authored-by: marcopeix <[email protected]>
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3 participants