Spatio-temporal point processes with deep non-stationary kernels
Authors: Zheng Dong, Xiuyuan Cheng, Yao Xie
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our proposed method s good performance and computational efficiency compared with the state-of-the-art on simulated and real data. Using extensive synthetic and real data experiments, we show the competitive performance of our proposed methods in both model recovery and event prediction compared with the state-of-the-art, such as the RNN-based and transformer-based models. |
| Researcher Affiliation | Academia | H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology Department of Mathematics, Duke University |
| Pseudocode | Yes | Algorithm 1 Model parameter estimation. Algorithm 2 Synthetic data generation. |
| Open Source Code | No | Codes will be released upon publication. |
| Open Datasets | Yes | Financial Transactions. (Du et al., 2016)., Stack Overflow (Leskovec and Krevl, 2014):, Southern California earthquake data provided by Southern California Earthquake Data Center (SCEDC) |
| Dataset Splits | No | Given the data set, we split 90% of the sequences as training set and 10% as testing set. |
| Hardware Specification | Yes | All experiments are implemented on Google Colaboratory (Pro version) with 25GB RAM and a Tesla T4 GPU. |
| Software Dependencies | No | The paper mentions using the Adam optimization method but does not provide specific version numbers for any software dependencies like PyTorch, TensorFlow, or other libraries used for implementation. |
| Experiment Setup | Yes | Details of Experimental setup. For RMTPP and NH we test embedding size of {32, 64, 128} and choose 64 for experiments. For THP we take the default experiment setting recommended by Zuo et al. (2020). For NSMPP we use the same model setting in Zhu et al. (2022) with rank 5. Each experiment is implemented by the following procedure: Given the data set, we split 90% of the sequences as training set and 10% as testing set. We use independent fully-connected neural networks with two-hidden layers for each basis function. Each layer contains 64 hidden nodes. The temporal rank of DNSK+Barrier is set to be 1 for synthetic data (i), (ii), (iv), (v), 2 for (vi), and 3 for (iii). The spatial rank is 1 for synthetic data (iv), (v) and 2 for (vi). The temporal and spatial rank for real data are both set to be 2 through cross validation. For each real data set, the Ď„max is chosen to be around T/4 and smax is 1 for each data set since the location space is normalized before training. The hyper-parameter of DNSK+Softplus are the same as DNSK+Barrier. For RMTPP, NH, and THP the batch size is 32 and the learning rate is 10 3. For others, the batch size is 64 and the learning rate is 10 1. The quantitative results are collected by running each experiment for 5 independent times. |