mTSeer: Interactive Visual Exploration of Models on Multivariate Time-series Forecast
mTSeer: Interactive Visual Exploration of Models on Multivariate Time-series Forecast
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DOI：
10.1145/3411764.3445083
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Ke Xu;Jun Yuan;Yifang Wang;Cláudio T. Silva;E. Bertini
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文献类型：
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作者：
Ke Xu;Jun Yuan;Yifang Wang;Cláudio T. Silva;E. Bertini
时间序列的预测为工业和机构决策提供了至关重要的信息，尽管已经开发出各种模型来促进预测过程，但它们使森林不一致。周期）。两个专家以及三个案例研究的定量评估。
Time-series forecasting contributes crucial information to industrial and institutional decision-making with multivariate time-series input. Although various models have been developed to facilitate the forecasting process, they make inconsistent forecasts. Thus, it is critical to select the model appropriately. The existing selection methods based on the error measures fail to reveal deep insights into the model’s performance, such as the identification of salient features and the impact of temporal factors (e.g., periods). This paper introduces mTSeer, an interactive system for the exploration, explanation, and evaluation of multivariate time-series forecasting models. Our system integrates a set of algorithms to steer the process, and rich interactions and visualization designs to help interpret the differences between models in both model and instance level. We demonstrate the effectiveness of mTSeer through three case studies with two domain experts on real-world data, qualitative interviews with the two experts, and quantitative evaluation of the three case studies.