use dev env for ci

[jmoralez]

No description provided.

[review-notebook-app]

Check out this pull request on 

See visual diffs & provide feedback on Jupyter Notebooks.

---

Powered by ReviewNB

[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	213.936	199.132	2571.33	10604.2
total_time	5.7018	8.0996	0.0085	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	4040.22	4110.79	5928.17	18859.2
total_time	8.1883	6.5322	0.0059	0.0047

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	5.3669	4.8847	0.0072	0.0062

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	6.8766	5.1089	0.007	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	11.4347	8.0766	0.0073	0.0069

Plot:

[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	213.936	199.132	2571.33	10604.2
total_time	28.8498	28.0388	0.0082	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	30.4336	34.5107	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	31.7793	23.4036	0.0073	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	33.4187	33.8868	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	31.9349	26.1681	0.0073	0.0066

Plot:

[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	213.936	199.132	2571.33	10604.2
total_time	29.0984	28.3741	0.0086	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	28.7698	28.8984	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	31.0528	31.9116	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	33.8381	28.3926	0.0071	0.0066

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	26.9356	28.8643	0.007	0.0064

Plot: