| 来源类型 | Monograph (IIASA Working Paper) |
| 规范类型 | 论文 |
| Towards Handling Uncertainty in Prognostic Scenarios: Advanced Learning from the Past. | |
| Zebrowski P; Jonas M; Jarnicka J | |
| 发表日期 | 2017 |
| 出版者 | IIASA, Laxenburg, Austria: WP-17-015 |
| 出版年 | 2017 |
| 语种 | 英语 |
| 摘要 | In this report we introduce the paradigm of learning from the past which is realized in a controlled prognostic context. It is a data-driven exploratory approach to assessing the limits to credibility of any expectations about the system’s future behavior which are based on a time series of a historical observations of the analyzed system. This horizon of the credible expectations is derived as the length of explainable outreach of the data, that is, the spatio-temporal extent which, in lieu of the knowledge contained in the historical observations, we are justified in believing contains the system’s future observations. Explainable outreach is of practical interest to stakeholders since it allows them to assess the credibility of scenarios produced by models of the analyzed system. It also indicates the scale of measures required to overcome the system’s inertia. In this report we propose a method of learning in a controlled prognostic context which is based on a polynomial regression technique. A polynomial regression model is used to understand the system’s dynamics, revealed by the sample of historical observations, while the explainable outreach is constructed around the extrapolated regression function. The proposed learning method was tested on various sets of synthetic data in order to identify its strengths and weaknesses, and formulate guidelines for its practical application. We also demonstrate how it can be used in context of earth system sciences by using it to derive the explainable outreach of historical anthropogenic CO2 emissions and atmospheric CO2 concentrations. We conclude that the most robust method of building the explainable outreach is based on linear regression. However, the explainable outreach of the analyzed datasets (representing credible expectations based on extrapolation of the linear trend) is rather short. |
| 主题 | Advanced Systems Analysis (ASA) |
| URL | http://pure.iiasa.ac.at/id/eprint/14834/ |
| 来源智库 | International Institute for Applied Systems Analysis (Austria) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/126076 |
| 推荐引用方式 GB/T 7714 | Zebrowski P,Jonas M,Jarnicka J. Towards Handling Uncertainty in Prognostic Scenarios: Advanced Learning from the Past.. 2017. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| WP-17-015.pdf（5363KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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