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[FEAT] What if - pricing in retail scenario #340

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merged 13 commits into from
May 10, 2024
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elephaint
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Adds simple use case for evaluating different pricing scenarios when forecasting product demand for a set of products in retail.

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github-actions bot commented May 6, 2024

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 11.9196 15.202 0.009 0.0051

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 14.0176 8.5715 0.0058 0.0052

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 142.394 196.363 269.23 1331.02
mape 0.0203 0.0234 0.0304 0.1692
mse 63464.8 123119 213677 4.68961e+06
total_time 11.5947 7.6257 0.0083 0.0075

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 522.427 353.528 398.956 1119.26
mape 0.069 0.0454 0.0512 0.1583
mse 966294 422332 656723 3.17316e+06
total_time 10.9425 14.1064 0.0076 0.0074

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 478.362 361.033 602.926 1340.95
mape 0.0622 0.046 0.0787 0.17
mse 805039 441118 1.61572e+06 6.04619e+06
total_time 13.8168 17.4895 0.0076 0.0074

Plot:

@elephaint elephaint requested a review from AzulGarza May 6, 2024 21:27
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review-notebook-app bot commented May 6, 2024

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mergenthaler commented on 2024-05-06T21:49:22Z
----------------------------------------------------------------

Maybe we can include a brief motivation or intro section. Something like:

"What if" scenarios in time series analysis are essential across various sectors for strategic insight and decision-making. In finance, they help investors anticipate market reactions to economic events, enabling proactive risk management. Supply chains utilize these analyses to prepare for demand shifts or supplier issues, enhancing operational resilience. Energy companies forecast the impact of demand fluctuations or equipment failures to optimize production and grid management. In healthcare, scenario analysis aids in resource allocation and patient care optimization by predicting potential changes in staff or patient volumes. Retailers leverage these scenarios to adjust to shifts in consumer behavior or economic conditions, ensuring inventory and marketing strategies remain aligned with market demands. By preparing for potential future conditions, organizations enhance their strategic flexibility and resilience.


elephaint commented on 2024-05-07T11:48:10Z
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Added a sentence

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mergenthaler commented on 2024-05-06T21:49:23Z
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Now that our docu is getting very comprehensive, here would be a good place to link to your guide on exogenous variables.


elephaint commented on 2024-05-07T11:55:28Z
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Added a callout-tip

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mergenthaler commented on 2024-05-06T21:49:24Z
----------------------------------------------------------------

This is great. Here I would relate this idea to the concept of elasticity. Maybe something like:

Elasticity measures how one variable responds to changes in another, commonly used to determine how price changes affect demand or supply. High elasticity indicates a significant response to small price changes, while low elasticity shows minimal response. This concept aids in setting pricing strategies and assessing the impact of economic policies on market dynamics.

Another helpful addition would be to include a comment on the underling assumptions. Maybe something like:

Creating "what if" scenarios by forecasting future values based on past observations, such as price changes, involves key assumptions. It presumes that historical events can predict future ones and that past data, like price fluctuations, is sufficient for meaningful analysis. Additionally, this method assumes stable relationships between variables over time and may not fully account for sudden market shifts or external influences. While these assumptions are necessary for modeling, they allow for strategic insights while acknowledging some degree of uncertainty in rapidly evolving environments.


elephaint commented on 2024-05-07T12:10:27Z
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Added a call-out note for elasticity reference and on assumptions.

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mergenthaler commented on 2024-05-06T21:49:24Z
----------------------------------------------------------------

It would be interesting to draw some conclusions. Somethih like:

In the graphs we can see that for specific products in specific regions the discount increases potential sales, while in other regions and and products, price change play a smaller effect on total demand. 


elephaint commented on 2024-05-07T12:17:37Z
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Added conclusion to the text above the plot

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Added a callout-tip


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Added a call-out note for elasticity reference and on assumptions.


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Added conclusion to the text above the plot


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github-actions bot commented May 7, 2024

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 3.4351 4.1647 0.0084 0.0048

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 3.7581 5.4938 0.0057 0.0048

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 142.394 196.363 269.23 1331.02
mape 0.0203 0.0234 0.0304 0.1692
mse 63464.7 123119 213677 4.68961e+06
total_time 6.0091 4.9949 0.0079 0.0069

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 522.427 353.528 398.956 1119.26
mape 0.069 0.0454 0.0512 0.1583
mse 966295 422332 656723 3.17316e+06
total_time 6.3068 3.7764 0.0074 0.0069

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 478.362 361.033 602.926 1340.95
mape 0.0622 0.046 0.0787 0.17
mse 805039 441118 1.61572e+06 6.04619e+06
total_time 5.4251 4.0191 0.0076 0.007

Plot:

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github-actions bot commented May 7, 2024

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 5.1185 3.4291 0.0084 0.0047

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 2.7238 4.9484 0.0059 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 142.394 196.363 269.23 1331.02
mape 0.0203 0.0234 0.0304 0.1692
mse 63464.7 123119 213677 4.68961e+06
total_time 3.6676 4.1126 0.008 0.007

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 522.427 353.528 398.956 1119.26
mape 0.069 0.0454 0.0512 0.1583
mse 966294 422332 656723 3.17316e+06
total_time 3.6999 4.8037 0.0077 0.0072

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 478.362 361.033 602.926 1340.95
mape 0.0622 0.046 0.0787 0.17
mse 805039 441118 1.61572e+06 6.04619e+06
total_time 5.3556 9.2854 0.0078 0.008

Plot:

@mergenthaler
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Thanks for the changes @elephaint! This is ready for your review @AzulGarza.

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github-actions bot commented May 8, 2024

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 14.7379 16.2159 0.009 0.0049

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 9.7305 13.3278 0.0059 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 14.5876 15.0963 0.0081 0.0073

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 13.2051 9.0379 0.0075 0.0071

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 13.2997 11.2499 0.0076 0.0073

Plot:

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AzulGarza commented on 2024-05-08T21:54:01Z
----------------------------------------------------------------

let's make colab changes in #349


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thanks @elephaint! i'm so sorry about asking for this change, but could you remove the colab part and wait to merge all colab changes in #349.

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github-actions bot commented May 9, 2024

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 6.1594 10.1481 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 4040.21 4110.79 5928.17 18859.2
total_time 6.0453 11.1343 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 10.7547 18.7403 0.0102 0.0089

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 26.3699 24.0487 0.007 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 29.9475 29.4429 0.007 0.0064

Plot:

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github-actions bot commented May 9, 2024

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 39.8134 37.0556 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 4040.22 4110.79 5928.17 18859.2
total_time 31.1906 42.0285 0.0052 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 31.0713 41.6263 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 36.7378 31.4584 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 33.2935 29.6788 0.0069 0.006

Plot:

@elephaint elephaint requested a review from AzulGarza May 9, 2024 07:53
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github-actions bot commented May 9, 2024

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 12.1653 13.4389 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 7.7063 13.4461 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 9.5095 15.4325 0.0076 0.0065

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 23.4231 23.5904 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 23.9699 26.1015 0.0074 0.0067

Plot:

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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 21.4599 17.5195 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 22.8177 21.7764 0.0053 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 24.2362 24.0598 0.0075 0.0065

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 21.6348 23.8251 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 22.5998 24.9677 0.0071 0.0067

Plot:

@elephaint elephaint merged commit 37433cb into main May 10, 2024
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@elephaint elephaint deleted the feature/what-if-pricing branch May 10, 2024 06:56
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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 37.886 28.3899 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 34.8192 35.9503 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 178.293 196.363 269.23 1331.02
mape 0.0234 0.0234 0.0304 0.1692
mse 121588 123119 213677 4.68961e+06
total_time 46.3236 36.7472 0.0069 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 34.3658 42.6421 0.0068 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 45.5418 77.4072 0.007 0.0066

Plot:

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