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{"references": ["Caltrans, 2020. Caltrans performance measurement system, california department of transportation. Accessed: 2020-04-30. URL http://pems.dot.ca.gov/", "Lai, G., Chang, W., Yang, Y., Liu, H., 2017. Modeling long and short-term temporal patterns with deep neural networks. CoRR abs/1703.07015.URL http://arxiv.org/abs/1703.07015", "Lai, G., 2017. multivariate-time-series-data. https://github.com/laiguokun/multivariate-time-series-data, accessed: 2020-05-04"]}
This dataset contains the San Francisco Traffic dataset used by Lai et al. (2017). It contains 862 hourly time series showing the road occupancy rates on the San Francisco Bay area freeways from 2015 to 2016.
San Francisco Traffic, forecasting, hourly series
San Francisco Traffic, forecasting, hourly series
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 423 | |
| downloads | 12K |

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