fix: note on morai improvements

[mergenthaler]

No description provided.

[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	1.763	1.4197	0.008	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	4040.21	4110.79	5928.17	18859.2
total_time	1.3124	1.4081	0.0049	0.0042

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	1.5362	1.5579	0.0069	0.006

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	2.203	1.5613	0.0066	0.0062

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	3.6095	1.6348	0.0068	0.0063

Plot:

[mergenthaler]

Thanks, @jmoralez, you are completely right.

[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	2.7314	1.7411	0.0078	0.0043

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	1.6746	1.596	0.0051	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	268.121	269.23	1331.02
mape	0.0234	0.0311	0.0304	0.1692
mse	121588	219457	213677	4.68961e+06
total_time	2.0452	2.3262	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	346.984	398.956	1119.26
mape	0.062	0.0437	0.0512	0.1583
mse	835120	403787	656723	3.17316e+06
total_time	2.8783	2.0649	0.007	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	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	3.7135	2.1055	0.0069	0.0064

Plot: