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tags:
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- count
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---
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
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***Pedestrian*** represents hourly pedestrian counts at 82 locations in Melbourne, Australia between 2009 and 2022 [1]. The processed dataset consists of 189,621 (univariate) time series, each of length 24. The task is to identify location based on the time series of counts. The dataset has been split into stratified random cross-validation folds.
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[1] City of Melbourne. (2022). Pedestrian counting system. <https://data.melbourne.vic.gov.au/explore/dataset/pedestrian-counting-system-monthly-counts-per-hour/information/>. CC BY 4.0.
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---
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tags:
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- time series
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- time series classification
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- monster
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- count
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license: cc-by-4.0
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pretty_name: Pedestrian
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size_categories:
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- 100K<n<1M
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>.
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|Pedestrian||
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|Category|Count|
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|Num. Examples|189,621|
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|Num. Channels|1|
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|Length|24|
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|Sampling Freq.|hourly|
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|Num. Classes|82|
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|License|[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)|
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|Citations|[1]|
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***Pedestrian*** represents hourly pedestrian counts at 82 locations in Melbourne, Australia between 2009 and 2022 [1]. The processed dataset consists of 189,621 (univariate) time series, each of length 24. The task is to identify location based on the time series of counts. The dataset has been split into stratified random cross-validation folds.
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[1] City of Melbourne. (2022). Pedestrian counting system. <https://data.melbourne.vic.gov.au/explore/dataset/pedestrian-counting-system-monthly-counts-per-hour/information/>. CC BY 4.0.
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