This paper aims to predict the traffic flow at one road segment based on
nearby traffic volume and weather conditions. Our team also discover the impact
of weather conditions and nearby traffic volume on the traffic flow at a target
point. The analysis results will help solve the problem of traffic flow
prediction and develop an optimal transport network with efficient traffic
movement and minimal traffic congestion. Hourly historical weather and traffic
flow data are selected to solve this problem. This paper uses model VAR(36)
with time trend and constant to train the dataset and forecast. With an RMSE of
565.0768111 on average, the model is considered appropriate although some
statistical tests implies that the residuals are unstable and non-normal. Also,
this paper points out some variables that are not useful in forecasting, which
helps simplify the data-collecting process when building the forecasting
system.

本研究旨在基于附近交通量和天气条件来预测一个道路段的交通流量，分析结果将有助于解决交通流量预测问题和开发具有高效交通运动和最小交通堵塞的最优化交通网络。

天气和附近交通流量对交通量影响的矢量自回归模型应用

An Application of Vector Autoregressive Model for Analyzing the Impact  of Weather And Nearby Traffic Flow On The Traffic Volume

An accurate and efficient forecasting system is imperative to the prevention
of emerging infectious diseases such as COVID-19 in public health. This system
requires accurate transient modeling, lower computation cost, and fewer
observation data. To tackle these three challenges, we propose a novel deep
learning approach using black-box knowledge distillation for both accurate and
efficient transmission dynamics prediction in a practical manner. First, we
leverage mixture models to develop an accurate, comprehensive, yet impractical
simulation system. Next, we use simulated observation sequences to query the
simulation system to retrieve simulated projection sequences as knowledge.
Then, with the obtained query data, sequence mixup is proposed to improve query
efficiency, increase knowledge diversity, and boost distillation model
accuracy. Finally, we train a student deep neural network with the retrieved
and mixed observation-projection sequences for practical use. The case study on
COVID-19 justifies that our approach accurately projects infections with much
lower computation cost when observation data are limited.

本文介绍了一种基于深度学习的黑盒知识蒸馏方法，用于对疾病传播进行准确和高效的预测，通过案例分析证明了该方法在有限观测数据条件下可以准确预测感染病例，具有较低的计算成本。

通过黑盒知识蒸馏的深度流行病学建模：COVID-19 的准确深度学习模型

Deep Epidemiological Modeling by Black-box Knowledge Distillation: An  Accurate Deep Learning Model for COVID-19

Water managers in the western United States (U.S.) rely on longterm forecasts
of temperature and precipitation to prepare for droughts and other wet weather
extremes. To improve the accuracy of these longterm forecasts, the U.S. Bureau
of Reclamation and the National Oceanic and Atmospheric Administration (NOAA)
launched the Subseasonal Climate Forecast Rodeo, a year-long real-time
forecasting challenge in which participants aimed to skillfully predict
temperature and precipitation in the western U.S. two to four weeks and four to
six weeks in advance. Here we present and evaluate our machine learning
approach to the Rodeo and release our SubseasonalRodeo dataset, collected to
train and evaluate our forecasting system.
Our system is an ensemble of two regression models. The first integrates the
diverse collection of meteorological measurements and dynamic model forecasts
in the SubseasonalRodeo dataset and prunes irrelevant predictors using a
customized multitask model selection procedure. The second uses only historical
measurements of the target variable (temperature or precipitation) and
introduces multitask nearest neighbor features into a weighted local linear
regression. Each model alone is significantly more accurate than the debiased
operational U.S. Climate Forecasting System (CFSv2), and our ensemble skill
exceeds that of the top Rodeo competitor for each target variable and forecast
horizon. Moreover, over 2011-2018, an ensemble of our regression models and
debiased CFSv2 improves debiased CFSv2 skill by 40-50% for temperature and
129-169% for precipitation. We hope that both our dataset and our methods will
help to advance the state of the art in subseasonal forecasting.

该论文描述了一个基于机器学习方法的预测系统，该系统利用历史气象测量值和动态模型预测，通过裁剪无关预测器和加权最近邻特征引入加权局部线性回归，显著提高了长期预测准确性。该系统在 Subseasonal Climate Forecast Rodeo 竞赛中表现优异，可帮助提高亚季节性气象预测的水平。