Multi-variable Adversarial Time-Series Forecast Model
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916270714126336 |
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| author | Chen, Xiaoqiao |
| author_facet | Chen, Xiaoqiao |
| contents | Short-term industrial enterprises power system forecasting is an important issue for both load control and machine protection. Scientists focus on load forecasting but ignore other valuable electric-meters which should provide guidance of power system protection. We propose a new framework, multi-variable adversarial time-series forecasting model, which regularizes Long Short-term Memory (LSTM) models via an adversarial process. The novel model forecasts all variables (may in different type, such as continue variables, category variables, etc.) in power system at the same time and helps trade-off process between forecasting accuracy of single variable and variable-variable relations. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples. The predict results of electricity consumption of industrial enterprises by multi-variable adversarial time-series forecasting model show that the proposed approach is able to achieve better prediction accuracy. We also applied this model to real industrial enterprises power system data we gathered from several large industrial enterprises via advanced power monitors, and got impressed forecasting results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00596 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-variable Adversarial Time-Series Forecast Model Chen, Xiaoqiao Machine Learning Short-term industrial enterprises power system forecasting is an important issue for both load control and machine protection. Scientists focus on load forecasting but ignore other valuable electric-meters which should provide guidance of power system protection. We propose a new framework, multi-variable adversarial time-series forecasting model, which regularizes Long Short-term Memory (LSTM) models via an adversarial process. The novel model forecasts all variables (may in different type, such as continue variables, category variables, etc.) in power system at the same time and helps trade-off process between forecasting accuracy of single variable and variable-variable relations. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples. The predict results of electricity consumption of industrial enterprises by multi-variable adversarial time-series forecasting model show that the proposed approach is able to achieve better prediction accuracy. We also applied this model to real industrial enterprises power system data we gathered from several large industrial enterprises via advanced power monitors, and got impressed forecasting results. |
| title | Multi-variable Adversarial Time-Series Forecast Model |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.00596 |