--- annotations_creators: - other language_creators: - other language: - en license: - other size_categories: - 1K1362 Followers - name: time_since_last_event sequence: float64 - name: time_since_start sequence: float64 - name: seq_idx dtype: int64 - name: dim_process dtype: int64 - name: seq_len dtype: int64 splits: - name: train num_bytes: 12155637 num_examples: 19200 - name: test num_bytes: 1482572 num_examples: 2400 - name: validation num_bytes: 1483960 num_examples: 2400 - config_name: taobao_easytpp features: - name: type_event sequence: class_label: names: '0': Item click category 0 '1': Item click category 1 '2': Item click category 2 '3': Item click category 3 '4': Item click category 4 '5': Item click category 5 '6': Item click category 6 '7': Item click category 7 '8': Item click category 8 '9': Item click category 9 '10': Item click category 10 '11': Item click category 11 '12': Item click category 12 '13': Item click category 13 '14': Item click category 14 '15': Item click category 15 '16': Item click category 16 - name: time_since_last_event sequence: float64 - name: time_since_start sequence: float64 - name: seq_idx dtype: int64 - name: dim_process dtype: int64 - name: seq_len dtype: int64 splits: - name: train num_bytes: 1264973 num_examples: 1600 - name: test num_bytes: 166371 num_examples: 200 - name: validation num_bytes: 165725 num_examples: 200 - config_name: taxi_easytpp features: - name: type_event sequence: class_label: names: '0': borough pick-up-or-drop-off combination 0 '1': borough pick-up-or-drop-off combination 1 '2': borough pick-up-or-drop-off combination 2 '3': borough pick-up-or-drop-off combination 3 '4': borough pick-up-or-drop-off combination 4 '5': borough pick-up-or-drop-off combination 5 '6': borough pick-up-or-drop-off combination 6 '7': borough pick-up-or-drop-off combination 7 '8': borough pick-up-or-drop-off combination 8 '9': borough pick-up-or-drop-off combination 9 - name: time_since_last_event sequence: float64 - name: time_since_start sequence: float64 - name: seq_idx dtype: int64 - name: dim_process dtype: int64 - name: seq_len dtype: int64 splits: - name: train num_bytes: 425735 num_examples: 1600 - name: test num_bytes: 81859 num_examples: 200 - name: validation num_bytes: 82275 num_examples: 200 - config_name: volcano_easytpp features: - name: type_event sequence: class_label: names: '0': eruption - name: time_since_last_event sequence: float64 - name: time_since_start sequence: float64 - name: seq_idx dtype: int64 - name: dim_process dtype: int64 - name: seq_len dtype: int64 splits: - name: train num_bytes: 114218 num_examples: 333 - name: test num_bytes: 17979 num_examples: 41 - name: validation num_bytes: 18309 num_examples: 42 - config_name: hawkes_dependent features: - name: type_event sequence: class_label: names: '0': simulated event type 0 '1': simulated event type 1 - name: time_since_last_event sequence: float64 - name: time_since_start sequence: float64 - name: seq_idx dtype: int64 - name: dim_process dtype: int64 - name: seq_len dtype: int64 splits: - name: train num_bytes: 8642638 num_examples: 19661 - name: test num_bytes: 1224355 num_examples: 2457 - name: validation num_bytes: 1232289 num_examples: 2458 - config_name: hawkes_1 features: - name: type_event sequence: class_label: names: '0': simulated event - name: time_since_last_event sequence: float64 - name: time_since_start sequence: float64 - name: seq_idx dtype: int64 - name: dim_process dtype: int64 - name: seq_len dtype: int64 splits: - name: train num_bytes: 1656093 num_examples: 800 - name: test num_bytes: 194623 num_examples: 100 - name: validation num_bytes: 191069 num_examples: 100 configs: - config_name: earthquake data_files: - split: train path: earthquake/train* - split: validation path: earthquake/validation* - split: test path: earthquake/test* - config_name: memetrack data_files: - split: train path: memetrack/train* - split: validation path: memetrack/validation* - split: test path: memetrack/test* - config_name: stackoverflow data_files: - split: train path: stackoverflow/train* - split: validation path: stackoverflow/validation* - split: test path: stackoverflow/test* - config_name: taxi_nyc_neighborhoods data_files: - split: train path: taxi_nyc_neighborhoods/train* - split: validation path: taxi_nyc_neighborhoods/validation* - split: test path: taxi_nyc_neighborhoods/test* - split: extra path: taxi_nyc_neighborhoods/extra* - config_name: synthea data_files: - split: train path: synthea/train* - split: validation path: synthea/validation* - split: test path: synthea/test* - config_name: spiketrains data_files: - split: train path: spiketrains/train* - split: validation path: spiketrains/validation* - split: test path: spiketrains/test* - config_name: crypto_transactions data_files: - split: train path: crypto_transactions/train* - split: validation path: crypto_transactions/validation* - split: test path: crypto_transactions/test* - config_name: human_activity data_files: - split: train path: human_activity/train* - split: validation path: human_activity/validation* - split: test path: human_activity/test* - config_name: '911' data_files: - split: train path: 911/train* - split: validation path: 911/validation* - split: test path: 911/test* - config_name: mooc data_files: - split: train path: mooc/train* - split: validation path: mooc/validation* - split: test path: mooc/test* - config_name: amazon_easytpp data_files: - split: train path: amazon_easytpp/train* - split: validation path: amazon_easytpp/validation* - split: test path: amazon_easytpp/test* - config_name: wikipedia data_files: - split: train path: wikipedia/train* - split: validation path: wikipedia/validation* - split: test path: wikipedia/test* - config_name: retweet_easytpp data_files: - split: train path: retweet_easytpp/train* - split: validation path: retweet_easytpp/validation* - split: test path: retweet_easytpp/test* - config_name: taobao_easytpp data_files: - split: train path: taobao_easytpp/train* - split: validation path: taobao_easytpp/validation* - split: test path: taobao_easytpp/test* - config_name: taxi_easytpp data_files: - split: train path: taxi_easytpp/train* - split: validation path: taxi_easytpp/validation* - split: test path: taxi_easytpp/test* - config_name: volcano_easytpp data_files: - split: train path: volcano_easytpp/train* - split: validation path: volcano_easytpp/validation* - split: test path: volcano_easytpp/test* - config_name: hawkes_dependent data_files: - split: train path: hawkes_dependent/train* - split: validation path: hawkes_dependent/validation* - split: test path: hawkes_dependent/test* - config_name: hawkes_1 data_files: - split: train path: hawkes_1/train* - split: validation path: hawkes_1/validation* - split: test path: hawkes_1/test* --- # MUSES: a benchmark for Marked Unevenly Spaced Event Sequences MUSES, a benchmark for **M**arked **U**nevenly **S**paced **E**vent **S**equences, is a collection of unevenly spaced time series datasets from various domains, containing marked events for training and evaluating prediction approaches. ## Table of Contents - [Dataset Card for MUSES](#dataset-card-for-muses) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [earthquake](#earthquake-1) - [memetrack](#memetrack-1) - [stackoverflow](#stackoverflow-1) - [taxi_nyc_neighborhoods](#taxi_nyc_neighborhoods-1) - [synthea](#synthea-1) - [spiketrains](#spiketrains-1) - [crypto_transactions](#crypto_transactions-1) - [human_activity](#human_activity-1) - [911](#911-1) - [mooc](#mooc-1) - [amazon_easytpp](#amazon_easytpp-1) - [wikipedia](#wikipedia-1) - [retweet_easytpp](#retweet_easytpp-1) - [taobao_easytpp](#taobao_easytpp-1) - [taxi_easytpp](#taxi_easytpp-1) - [volcano_easytpp](#volcano_easytpp-1) - [hawkes_dependent](#hawkes_dependent-1) - [hawkes_1](#hawkes_1-1) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Repository:** [MUSES_Preprocessing on GitHub](https://github.com/clrfl/MUSES_Preprocessing) - **Paper:** TODO Jimmy - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Size of downloaded dataset files:** 2.24 GB - **Size of the generated dataset:** 4.32 GB - **Total amount of disk used:** 6.56 GB ### Languages All the columns and classes (when textual) in MUSES are in English (BCP-47 `en`) ## Dataset Structure ### Data Instances All datasets formatting follows a similar structure. ``` { "datetime": [2020-06-10T14:27:37.963000000, 2020-06-20T21:23:06.980000000, 2020-10-15T13:36:16.687000000], "type_event": [7, 2, 15], "time_since_last_event": [0.0, 246.92472694444444, 2800.2193630555557], "time_since_start": [0.0, 246.92472694444444, 3047.14409], "seq_idx": 1, "dim_process": 50, "seq_len": 3 } ``` #### earthquake - **Size of downloaded dataset files:** 2.77 MB - **Size of the generated dataset:** 3.06 MB - **Total amount of disk used:** 5.83 MB #### memetrack - **Size of downloaded dataset files:** 569.01 MB - **Size of the generated dataset:** 993.72 MB - **Total amount of disk used:** 1562.72 MB #### stackoverflow - **Size of downloaded dataset files:** 110.71 MB - **Size of the generated dataset:** 143.91 MB - **Total amount of disk used:** 254.62 MB #### taxi_nyc_neighborhoods - **Size of downloaded dataset files:** 968.44 MB - **Size of the generated dataset:** 2031.69 MB - **Total amount of disk used:** 3000.14 MB #### synthea - **Size of downloaded dataset files:** 7.65 MB - **Size of the generated dataset:** 14.56 MB - **Total amount of disk used:** 22.21 MB #### spiketrains - **Size of downloaded dataset files:** 8.14 MB - **Size of the generated dataset:** 20.57 MB - **Total amount of disk used:** 28.71 MB #### crypto_transactions - **Size of downloaded dataset files:** 393.35 MB - **Size of the generated dataset:** 748.14 MB - **Total amount of disk used:** 1141.49 MB #### human_activity - **Size of downloaded dataset files:** 0.20 MB - **Size of the generated dataset:** 0.22 MB - **Total amount of disk used:** 0.42 MB #### 911 - **Size of downloaded dataset files:** 8.42 MB - **Size of the generated dataset:** 21.63 MB - **Total amount of disk used:** 30.05 MB #### mooc - **Size of downloaded dataset files:** 3.97 MB - **Size of the generated dataset:** 10.29 MB - **Total amount of disk used:** 14.26 MB #### amazon_easytpp - **Size of downloaded dataset files:** 7.81 MB - **Size of the generated dataset:** 10.41 MB - **Total amount of disk used:** 18.22 MB #### wikipedia - **Size of downloaded dataset files:** 127.20 MB - **Size of the generated dataset:** 238.24 MB - **Total amount of disk used:** 365.43 MB #### retweet_easytpp - **Size of downloaded dataset files:** 15.12 MB - **Size of the generated dataset:** 64.49 MB - **Total amount of disk used:** 79.62 MB #### taobao_easytpp - **Size of downloaded dataset files:** 1.60 MB - **Size of the generated dataset:** 2.84 MB - **Total amount of disk used:** 4.43 MB #### taxi_easytpp - **Size of downloaded dataset files:** 0.59 MB - **Size of the generated dataset:** 1.88 MB - **Total amount of disk used:** 2.47 MB #### volcano_easytpp - **Size of downloaded dataset files:** 0.15 MB - **Size of the generated dataset:** 0.23 MB - **Total amount of disk used:** 0.38 MB #### hawkes_dependent - **Size of downloaded dataset files:** 11.10 MB - **Size of the generated dataset:** 15.84 MB - **Total amount of disk used:** 26.94 MB #### hawkes_1 - **Size of downloaded dataset files:** 2.04 MB - **Size of the generated dataset:** 2.49 MB - **Total amount of disk used:** 4.54 MB ### Data Fields The data fields are the same among all datasets and splits. The datetime column is optional and left out when no data is available. - `type_event`: a classification label (`int64`) sequence. - `time_since_last_event`: a `float64` sequence of inter-event times. - `time_since_start`: a `float64` sequence of event timestamps since sequence start. - `seq_idx`: an `int64` feature uniquely identifying sequences in the split. - `dim_process`: an `int64` feature representing total number of classes in the dataset. - `seq_len`: an `int64` feature containing sequence length. - `datetime`: (if avail.) a `timestamp[ns]` sequence, containing event datetime if available. The classification labels differ for each dataset. The corresponding labels for each event type can be read from metadata like this ```python >>> import datasets as ds >>> meta_data = ds.load_dataset_builder("ddrg/MUSES", "DATASET-NAME") >>> [label for label in enumerate(meta_data.info.features["type_event"].feature.names)] ``` ### Data Splits | | train|validation| test| |-------------------|------:|---------:|-----:| |earthquake | 1724| 216| 215| |memetrack |1264794| 158100|158099| |stackoverflow | 42838| 5354| 5355| |synthea | 9198| 1150| 1149| |spiketrains | 480| 60| 60| |crypto_transactions|1408527| 176066|176066| |human_activity | 175| 22| 22| |911 | 1355| 169| 170| |mooc | 5638| 704| 705| |amazon_easytpp | 7382| 922| 923| |wikipedia | 907765| 113470|113471| |retweet_easytpp | 19200| 2400| 2400| |taobao_easytpp | 1600| 200| 200| |taxi_easytpp | 1600| 200| 200| |volcano_easytpp | 333| 42| 41| |hawkes_dependent | 19661| 2458| 2457| |hawkes_1 | 800| 100| 100| #### taxi_nyc_neighborhoods | | train|validation| test| extra| |----------------------|-----:|---------:|----:|-----:| |taxi_nyc_neighborhoods|273060| 34132|34133|106020| ## Dataset Creation ### Curation Rationale A dataset has to comply with the following hard requirements to be eligible for MUSES. 1. Inherently unevenly spaced 2. Time series minimum length of 2 3. Known license 4. Used in Literature 5. Replicably described data collection and processing 6. Dataset has practical relevance or is commonly used for evaluation 7. Maximum of 100 M events 8. Nominal classes or common class analogy 9. Commonly used in unevenly spaced time or TPP forecasting Further, we aim to fulfull the following variety constraints by dataset composition. 1. Coverage of many different domains 2. Variety in dataset sizes 3. Variety in sequence lengths 4. Variety in problem difficulties 5. Variety in problem complexity 6. Variety in class balances See our paper for further information. ### Source Data #### Initial Data Collection and Normalization Data was sourced and preprocessed like in other works, unless explicitly stated otherwise in our paper. See [Citation Information](#citation-information) for details. ### Personal and Sensitive Information Data is deidentified or all personal and sensitive information was removed. ## Considerations for Using the Data ### Discussion of Biases There are no ethical biases we are aware of. ### Other Known Limitations Class imbalances are common in event sequences. See the following paper for further information. ```TODO JIMMY``` ## Additional Information ### Dataset Curators [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Licensing Information The primary MUSES tasks are built on and derived from existing datasets. We refer users to the original licenses accompanying each dataset. | | License| Other| |----------------------|------------------:|------------------------------------------------------------------------------------------------------------------:| |earthquake | Public Domain| | |memetrack | 3-Clause BSD|[see LICENSE/memetrack](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/memetrack) | |stackoverflow | CC BY-SA|[see LICENSE/stackoverflow](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/stackoverflow) | |taxi_nyc_neighborhoods| Public Domain| | |synthea | Apache 2.0|[see LICENSE/synthea](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/synthea) | |spiketrains | CC0|[see LICENSE/spiketrains](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/spiketrains) | |crypto_transactions | 3-Clause BSD|[see LICENSE/crypto_transactions](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/crypto_transactions)| |human_activity | CC-BY|[see LICENSE/human_activity](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/human_activity) | |911 | ODC ODbL|[see LICENSE/911](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/911) | |mooc |Scientific Use Only|[see LICENSE/mooc](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/mooc) | |amazon_easytpp | Apache 2.0|[see LICENSE/amazon_easytpp](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/amazon_easytpp) | |wikipedia | CC BY-SA|[see LICENSE/wikipedia](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/wikipedia) | |retweet_easytpp | Apache 2.0|[see LICENSE/retweet_easytpp](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/retweet_easytpp) | |taobao_easytpp | Apache 2.0|[see LICENSE/taobao_easytpp](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/taobao_easytpp) | |taxi_easytpp | Apache 2.0|[see LICENSE/taxi_easytpp](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/taxi_easytpp) | |volcano_easytpp | Apache 2.0|[see LICENSE/volcano_easytpp](https://huggingface.co/datasets/ddrg/MUSES/tree/main/LICENSE/volcano_easytpp) | |hawkes_dependent | Public Domain| | |hawkes_1 | Public Domain| | ### Citation Information We encourage you to use the following BibTeX citation for MUSES itself: ``` # TODO @Jimmy Add here Final Paper ``` If you use MUSES, please also cite all the individual datasets you use, both to give the original authors their due credit and because venues will expect papers to describe the data they evaluate on. The following provides BibTeX for all of the MUSES tasks. #### earthquake ``` @article{stockman2024earthquakenpp, title={EarthquakeNPP: A Benchmark for Earthquake Forecasting with Neural Point Processes}, author={Stockman, Samuel and Lawson, Daniel and Werner, Maximilian}, journal={arXiv preprint arXiv:2410.08226}, year={2024} } ``` #### memetrack ``` @inproceedings{leskovec2009meme, title={Meme-tracking and the dynamics of the news cycle}, author={Leskovec, Jure and Backstrom, Lars and Kleinberg, Jon}, booktitle={Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, pages={497--506}, year={2009} } @article{mei2017neural, title={The neural hawkes process: A neurally self-modulating multivariate point process}, author={Mei, Hongyuan and Eisner, Jason M}, journal={Advances in neural information processing systems}, volume={30}, year={2017} } ``` #### stackoverflow ``` @inproceedings{du2016recurrent, title={Recurrent marked temporal point processes: Embedding event history to vector}, author={Du, Nan and Dai, Hanjun and Trivedi, Rakshit and Upadhyay, Utkarsh and Gomez-Rodriguez, Manuel and Song, Le}, booktitle={Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining}, pages={1555--1564}, year={2016} } ``` #### taxi_nyc_neighborhoods ``` @inproceedings{du2016recurrent, title={Recurrent marked temporal point processes: Embedding event history to vector}, author={Du, Nan and Dai, Hanjun and Trivedi, Rakshit and Upadhyay, Utkarsh and Gomez-Rodriguez, Manuel and Song, Le}, booktitle={Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining}, pages={1555--1564}, year={2016} } ``` #### synthea ``` @article{walonoski2018synthea, title={Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record}, author={Walonoski, Jason and Kramer, Mark and Nichols, Joseph and Quina, Andre and Moesel, Chris and Hall, Dylan and Duffett, Carlton and Dube, Kudakwashe and Gallagher, Thomas and McLachlan, Scott}, journal={Journal of the American Medical Informatics Association}, volume={25}, number={3}, pages={230--238}, year={2018}, publisher={Oxford University Press} } @inproceedings{enguehard2020neural, title={Neural temporal point processes for modelling electronic health records}, author={Enguehard, Joseph and Busbridge, Dan and Bozson, Adam and Woodcock, Claire and Hammerla, Nils}, booktitle={Machine Learning for Health}, pages={85--113}, year={2020}, organization={PMLR} } ``` #### spiketrains ``` @article{stetter2012model, title={Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals}, author={Stetter, Olav and Battaglia, Demian and Soriano, Jordi and Geisel, Theo}, year={2012}, publisher={Public Library of Science San Francisco, USA} } @article{linderman2015scalable, title={Scalable bayesian inference for excitatory point process networks}, author={Linderman, Scott W and Adams, Ryan P}, journal={arXiv preprint arXiv:1507.03228}, year={2015} } ``` #### crypto_transactions ``` @article{shamsi2022chartalist, title={Chartalist: Labeled graph datasets for utxo and account-based blockchains}, author={Shamsi, Kiarash and Victor, Friedhelm and Kantarcioglu, Murat and Gel, Yulia and Akcora, Cuneyt G}, journal={Advances in Neural Information Processing Systems}, volume={35}, pages={34926--34939}, year={2022} } @inproceedings{du2016recurrent, title={Recurrent marked temporal point processes: Embedding event history to vector}, author={Du, Nan and Dai, Hanjun and Trivedi, Rakshit and Upadhyay, Utkarsh and Gomez-Rodriguez, Manuel and Song, Le}, booktitle={Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining}, pages={1555--1564}, year={2016} } ``` #### human_activity ``` @article{cook2012casas, title={CASAS: A smart home in a box}, author={Cook, Diane J and Crandall, Aaron S and Thomas, Brian L and Krishnan, Narayanan C}, journal={Computer}, volume={46}, number={7}, pages={62--69}, year={2012}, publisher={IEEE} } @article{fortino2021predicting, title={Predicting activities of daily living via temporal point processes: Approaches and experimental results}, author={Fortino, Giancarlo and Guzzo, Antonella and Ianni, Michele and Leotta, Francesco and Mecella, Massimo}, journal={Computers \& Electrical Engineering}, volume={96}, pages={107567}, year={2021}, publisher={Elsevier} } ``` #### 911 ``` @inproceedings{zuo2020transformer, title={Transformer hawkes process}, author={Zuo, Simiao and Jiang, Haoming and Li, Zichong and Zhao, Tuo and Zha, Hongyuan}, booktitle={International conference on machine learning}, pages={11692--11702}, year={2020}, organization={PMLR} } ``` #### mooc ``` @inproceedings{kumar2019predicting, title={Predicting dynamic embedding trajectory in temporal interaction networks}, author={Kumar, Srijan and Zhang, Xikun and Leskovec, Jure}, booktitle={Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining}, pages={1269--1278}, year={2019} } @article{shchur2019intensity, title={Intensity-free learning of temporal point processes}, author={Shchur, Oleksandr and Bilo{\v{s}}, Marin and G{\"u}nnemann, Stephan}, journal={arXiv preprint arXiv:1909.12127}, year={2019} } ``` #### amazon_easytpp ``` @inproceedings{ni2019justifying, title={Justifying recommendations using distantly-labeled reviews and fine-grained aspects}, author={Ni, Jianmo and Li, Jiacheng and McAuley, Julian}, booktitle={Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP)}, pages={188--197}, year={2019} } @article{xue2023easytpp, title={Easytpp: Towards open benchmarking temporal point processes}, author={Xue, Siqiao and Shi, Xiaoming and Chu, Zhixuan and Wang, Yan and Hao, Hongyan and Zhou, Fan and Jiang, Caigao and Pan, Chen and Zhang, James Y and Wen, Qingsong and others}, journal={arXiv preprint arXiv:2307.08097}, year={2023} } ``` #### wikipedia ``` @inproceedings{kumar2019predicting, title={Predicting dynamic embedding trajectory in temporal interaction networks}, author={Kumar, Srijan and Zhang, Xikun and Leskovec, Jure}, booktitle={Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining}, pages={1269--1278}, year={2019} } @article{bosser2023predictive, title={On the predictive accuracy of neural temporal point process models for continuous-time event data}, author={Bosser, Tanguy and Taieb, Souhaib Ben}, journal={arXiv preprint arXiv:2306.17066}, year={2023} } ``` #### retweet_easytpp ``` @inproceedings{zhao2015seismic, title={Seismic: A self-exciting point process model for predicting tweet popularity}, author={Zhao, Qingyuan and Erdogdu, Murat A and He, Hera Y and Rajaraman, Anand and Leskovec, Jure}, booktitle={Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining}, pages={1513--1522}, year={2015} } @article{xue2023easytpp, title={Easytpp: Towards open benchmarking temporal point processes}, author={Xue, Siqiao and Shi, Xiaoming and Chu, Zhixuan and Wang, Yan and Hao, Hongyan and Zhou, Fan and Jiang, Caigao and Pan, Chen and Zhang, James Y and Wen, Qingsong and others}, journal={arXiv preprint arXiv:2307.08097}, year={2023} } ``` #### taobao_easytpp ``` @article{xue2022hypro, title={Hypro: A hybridly normalized probabilistic model for long-horizon prediction of event sequences}, author={Xue, Siqiao and Shi, Xiaoming and Zhang, James and Mei, Hongyuan}, journal={Advances in Neural Information Processing Systems}, volume={35}, pages={34641--34650}, year={2022} } @article{xue2023easytpp, title={Easytpp: Towards open benchmarking temporal point processes}, author={Xue, Siqiao and Shi, Xiaoming and Chu, Zhixuan and Wang, Yan and Hao, Hongyan and Zhou, Fan and Jiang, Caigao and Pan, Chen and Zhang, James Y and Wen, Qingsong and others}, journal={arXiv preprint arXiv:2307.08097}, year={2023} } ``` #### taxi_easytpp ``` @article{xue2023easytpp, title={Easytpp: Towards open benchmarking temporal point processes}, author={Xue, Siqiao and Shi, Xiaoming and Chu, Zhixuan and Wang, Yan and Hao, Hongyan and Zhou, Fan and Jiang, Caigao and Pan, Chen and Zhang, James Y and Wen, Qingsong and others}, journal={arXiv preprint arXiv:2307.08097}, year={2023} } ``` #### volcano_easytpp ``` @article{bebbington2014long, title={Long-term forecasting of volcanic explosivity}, author={Bebbington, MS}, journal={Geophysical Journal International}, volume={197}, number={3}, pages={1500--1515}, year={2014}, publisher={Oxford University Press} } @misc{easytpp-github-volcano, title = {EasyTemporalPointProcess GitHub Volcano Preprocessing}, author = {Xue, Siqiao and Shi, Xiaoming and Chu, Zhixuan and Wang, Yan and Hao, Hongyan and Zhou, Fan and Jiang, Caigao and Pan, Chen and Zhang, James Y and Wen, Qingsong and others}, url = {https://github.com/ant-research/EasyTemporalPointProcess/blob/main/examples/script_data_processing/volcano.py} } ``` #### hawkes_dependent ``` @inproceedings{enguehard2020neural, title={Neural temporal point processes for modelling electronic health records}, author={Enguehard, Joseph and Busbridge, Dan and Bozson, Adam and Woodcock, Claire and Hammerla, Nils}, booktitle={Machine Learning for Health}, pages={85--113}, year={2020}, organization={PMLR} } ``` #### hawkes_1 ``` @article{omi2019fully, title={Fully neural network based model for general temporal point processes}, author={Omi, Takahiro and Aihara, Kazuyuki and others}, journal={Advances in neural information processing systems}, volume={32}, year={2019} } ``` ### Contributions Thanks to [@Moritz Tschöpe](https://github.com/clrfl) and [@Jimmy Pöhlmann](https://github.com/JP-SystemsX) for adding this dataset.