By its emissions of greenhouse gases, economic activity is the source of climate change which affects pandemics that in turn can impact badly on economies. Across the three highly interacting disciplines in our title, time-series observations are measured at vastly different data frequencies: very low frequency at 1000-year intervals for paleoclimate, through annual, monthly to intra-daily for current climate; weekly and daily for pandemic data; annual, quarterly and monthly for economic data, and seconds or nano-seconds in finance. Nevertheless, there are important commonalities to economic, climate and pandemic time series. First, time series in all three disciplines are subject to non-stationarities from evolving stochastic trends and sudden distributional shifts, as well as data revisions and changes to data measurement systems. Next, all three have imperfect and incomplete knowledge of their data generating processes from changing human behaviour, so must search for reasonable empirical modeling approximations. Finally, all three need forecasts of likely future outcomes to plan and adapt as events unfold, albeit again over very different horizons. We consider how these features shape the formulation and selection of forecasting models to tackle their common data features yet distinct problems.

中文翻译：

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预测面临的经济变化、气候变化和不断演变的流行病

通过排放温室气体，经济活动是气候变化的根源，它会影响大流行病，反过来又会对经济产生严重影响。在我们标题中的三个高度相互作用的学科中，时间序列观测以截然不同的数据频率进行测量：古气候以 1000 年为间隔的非常低的频率，对于当前气候，从年度、月度到日内；每周和每天的大流行数据；经济数据为年度、季度和月度数据，金融数据为秒或纳秒。然而，经济、气候和大流行时间序列存在重要的共性。首先，所有三个学科的时间序列都受到不断变化的随机趋势和突然的分布变化以及数据修订和数据测量系统变化的非平稳性影响。下一个，所有这三个人都对不断变化的人类行为产生的数据生成过程有不完善和不完整的了解，因此必须寻找合理的经验建模近似值。最后，这三者都需要对可能的未来结果进行预测，以便随着事件的发展进行规划和调整，尽管再次发生在非常不同的范围内。我们考虑这些特征如何影响预测模型的制定和选择，以解决它们共同的数据特​​征和不同的问题。