[DOC] Add azure callouts to use cases

[elephaint]

• Adds Azure callouts after instantiating NixtlaClient and after calling forecast / cross-validation / detect_anomalies

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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	2.2285	1.8811	0.0084	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	2.6114	3.4196	0.0052	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	2.0737	3.4325	0.0074	0.0063

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	3.1118	2.5639	0.0069	0.0064

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	6.4498	2.6695	0.0073	0.0067

Plot:

[AzulGarza]

thank you so much @elephaint! i've deployed to readme dot com the version on this branch, and it looks really cool! the only thing is that we have a strange rendering when instantiating the NixtlClient class:

i think we could fix this problem:

1. by using just one line:

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

2. multiple ticks:

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

wdyt?

[elephaint]

thank you so much @elephaint! i've deployed to readme dot com the version on this branch, and it looks really cool! the only thing is that we have a strange rendering when instantiating the NixtlClient class:

i think we could fix this problem:

1. by using just one line:

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

2. multiple ticks:

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

wdyt?

I like the one-liner, keeps things simple :) I'll change it to that, ok?

[github-actions]

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	4.1216	4.6063	0.0083	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	6.2888	1.9095	0.0053	0.0045

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	3.2113	3.7202	0.0075	0.0063

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	6.0581	4.7669	0.007	0.0064

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	6.4019	3.5384	0.0071	0.0065

Plot:

[AzulGarza]

thanks @elephaint! lgtm😍