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[FEAT] TabPFN test on VN1-competition #601

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elephaint
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Work done by @marcopeix, few small changes from my end.

Run the following commands from a terminal, using a nixtla environment (see Contributing.md), from folder experiments\vn1-competition,:

  1. make download_data
  2. pip install tabpfn-time-series datasets
  3. python experiments/vn1-competition/test_tabpfn.py

Following results should display (takes a loooonng time):

image

@elephaint elephaint requested a review from marcopeix February 11, 2025 08:28
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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 213.936 199.132 2571.33 10604.2
total_time 1.1132 1.2065 0.0046 0.0034

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 0.9212 0.9102 0.0038 0.0033

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 1.5472 1.9115 0.0049 0.0043

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 1.4778 1.1388 0.005 0.0044

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 1.3473 1.3752 0.0051 0.0046

Plot:

@marcopeix
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Minor comment: we should place this in experiments/vn1-competition/src/.
@cchallu, do we want to merge this?

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2 participants