End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay's data center) and the preliminary results demonstrate efficacy of our proposed method

Emission factor modelling for light vehicles within the European Artemis model

International audienceThe emission models for atmospheric pollutants have been updated and strongly improved for the road light vehicles. This development is based on a wide and specific measurement campaign, with more than 150 vehicles and about 3500 tests for a large number of pollutants. The results of these measurements are included in a database especially designed, available and open to future European measurements data. The Artemis model for light vehicles contains a set of complementary sub-models. The base model calculates the hot emissions for each vehicle category according to the driving behaviour. It contains 5 alternative models: The main model considers traffic situations (discrete model), with emission factors for each of them; A simplified model, built on the same data, takes into account the driving behaviour through the average speed (continuous model); A continuous model, so-called kinematic, considers a limited number of aggregated kinematic parameters; 2 instantaneous models consider some instantaneous parameters as instantaneous speed. These models are associated to models taking into account the influence of several parameters, as cold start, using of auxiliaries like air conditioning, vehicle mileage, ambient air temperature and humidity, road slope and vehicle load

Emission factor modelling for light vehicles within the European Artemis model

International audienceThe emission models for atmospheric pollutants have been updated and strongly improved for the road light vehicles. This development is based on a wide and specific measurement campaign, with more than 150 vehicles and about 3500 tests for a large number of pollutants. The results of these measurements are included in a database especially designed, available and open to future European measurements data. The Artemis model for light vehicles contains a set of complementary sub-models. The base model calculates the hot emissions for each vehicle category according to the driving behaviour. It contains 5 alternative models: The main model considers traffic situations (discrete model), with emission factors for each of them; A simplified model, built on the same data, takes into account the driving behaviour through the average speed (continuous model); A continuous model, so-called kinematic, considers a limited number of aggregated kinematic parameters; 2 instantaneous models consider some instantaneous parameters as instantaneous speed. These models are associated to models taking into account the influence of several parameters, as cold start, using of auxiliaries like air conditioning, vehicle mileage, ambient air temperature and humidity, road slope and vehicle load

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities, states, and countries. The hierarchical time series (HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation. In the previous works, hierarchical information is only integrated in the reconciliation step to maintain coherency, but not in forecasting step for accuracy improvement. In this paper, we propose two novel tree-based feature integration mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure to improve the forecasting performance. Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only,we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e.g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks. Experiments on real-world datasets demonstrate that our tree-based feature integration mechanism achieves superior performances on hierarchical forecasting tasks compared to the state-of-the-art methods, and our neural optimization networks can be applied to real-world tasks effectively without any additional effort under coherence and task-based constraint