nixtla [FIX] Colab-flag

Adds Colab flags to all notebooks
Adds Colab badge to capabilities notebooks

May 08 '24 15:05 elephaint

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May 08 '24 15:05 review-notebook-app[bot]

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	4.1452	3.7495	0.0084	0.005

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	2.6003	2.6576	0.0061	0.0049

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	2.125	3.2458	0.0078	0.0067

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	3.4025	2.0701	0.0075	0.007

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	5.3645	3.8609	0.0075	0.007

Plot:

May 08 '24 15:05 github-actions[bot]

thank you so much @elephaint, it seems it involved a lot of effort. i'm very sorry about asking a change to his pr, but would you be open to implementing the following logic? i think it can improve readability and might be easier to maintain:

define IN_COLAB = 'google.colab' in sys.modules in nixtla/utils so we can import it instead of defining each time.

import all the required deps for colab in one cell:
#| hide
if not IN_COLAB:
    from nixtla.utils import colab_badge
    rom dotenv import load_dotenv

@AzulGarza in Colab nixtla is not installed, so we can't import IN_COLAB when it's part of nixtla. Unless the first line in Colab is %pip install nixtla', but then we can't make that install conditional on being in Colab. I can include %pip install nixtla`as first line everywhere? Let me know what you think.

I refactored based on your second comment, good point!

May 09 '24 06:05 elephaint

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	3.1936	4.3738	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	3.7778	3.3405	0.005	0.0043

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	4.7668	3.1739	0.0072	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	4.3474	4.2618	0.0067	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	5.8297	3.7943	0.007	0.0066

Plot:

May 09 '24 07:05 github-actions[bot]

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	22.3491	7.1195	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	8.9854	13.1764	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	13.794	15.2337	0.0075	0.0067

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	835121	422332	656723	3.17316e+06
total_time	13.924	16.3337	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	21.4664	24.5086	0.0069	0.0064

Plot:

May 09 '24 07:05 github-actions[bot]

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	31.292	29.183	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	33.5923	38.7194	0.0054	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	46.7641	35.2284	0.0074	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	76.2885	42.9577	0.0072	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	34.1787	37.6032	0.0071	0.0066

Plot:

May 10 '24 08:05 github-actions[bot]

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	1.9095	2.3632	0.0079	0.0044

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	3.0912	3.1779	0.0052	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	196.363	269.23	1331.02
mape	0.0234	0.0234	0.0304	0.1692
mse	121588	123119	213677	4.68961e+06
total_time	2.2364	2.3899	0.0076	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	3.9291	3.9888	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	4.1406	4.102	0.0071	0.0066

Plot:

May 10 '24 09:05 github-actions[bot]

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	3.5778	3.025	0.0083	0.0044

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	2.7831	2.3123	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	196.363	269.23	1331.02
mape	0.0234	0.0234	0.0304	0.1692
mse	121588	123119	213677	4.68961e+06
total_time	3.6664	2.0793	0.0075	0.0077

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	3.1448	5.2197	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	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	4.0553	4.5764	0.007	0.0063

Plot:

May 10 '24 10:05 github-actions[bot]

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	4.3805	2.9556	0.0093	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	2.9246	4.1057	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	4.8346	4.5169	0.0076	0.0066

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	4.1531	4.1328	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	5.0645	2.8309	0.007	0.0065

Plot:

May 10 '24 10:05 github-actions[bot]

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	2.2521	1.8874	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	3.3003	3.8466	0.0055	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	3.3213	4.2079	0.0072	0.0077

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	4.0322	3.6455	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	4.7531	3.905	0.0073	0.0067

Plot:

May 10 '24 10:05 github-actions[bot]

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	4.3843	2.8333	0.008	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	2.9206	3.0044	0.0051	0.0043

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	1.9822	4.3807	0.0071	0.0061

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	4.0309	3.1283	0.0069	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	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	4.2844	4.5795	0.007	0.0065

Plot:

May 10 '24 10:05 github-actions[bot]

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	2.1644	3.2636	0.008	0.0044

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	3.1592	4.7292	0.0051	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	196.363	269.23	1331.02
mape	0.0234	0.0234	0.0304	0.1692
mse	121588	123119	213677	4.68961e+06
total_time	3.3469	2.3723	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	3.4133	4.829	0.0077	0.0099

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	4.4506	5.1124	0.0068	0.0061

Plot:

May 13 '24 07:05 github-actions[bot]

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[FIX] Colab-flag

Experiment 1: air-passengers

Description:

Results:

Plot:

Experiment 2: air-passengers

Description:

Results:

Plot:

Experiment 3: electricity-multiple-series

Description:

Results:

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Experiment 4: electricity-multiple-series

Description:

Results:

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Experiment 5: electricity-multiple-series

Description:

Results:

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Experiment 1: air-passengers

Description:

Results:

Plot:

Experiment 2: air-passengers

Description:

Results:

Plot:

Experiment 3: electricity-multiple-series

Description:

Results:

Plot:

Experiment 4: electricity-multiple-series

Description:

Results:

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Experiment 5: electricity-multiple-series

Description:

Results:

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Experiment 1: air-passengers

Description:

Results:

Plot:

Experiment 2: air-passengers

Description:

Results:

Plot:

Experiment 3: electricity-multiple-series

Description:

Results:

Plot:

Experiment 4: electricity-multiple-series

Description:

Results:

Plot:

Experiment 5: electricity-multiple-series

Description:

Results:

Plot:

Experiment 1: air-passengers

Description:

Results:

Plot:

Experiment 2: air-passengers

Description:

Results:

Plot:

Experiment 3: electricity-multiple-series

Description:

Results:

Plot:

Experiment 4: electricity-multiple-series

Description:

Results:

Plot:

Experiment 5: electricity-multiple-series

Description:

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