Tootfinder

Constructing hierarchical time series through clustering: Is there an optimal way for forecasting?
Bohan Zhang, Anastasios Panagiotelis, Han Li
https://arxiv.org/abs/2404.06064

Constructing hierarchical time series through clustering: Is there an optimal way for forecasting?
Forecast reconciliation has attracted significant research interest in recent years, with most studies taking the hierarchy of time series as given. We extend existing work that uses time series clustering to construct hierarchies, with the goal of improving forecast accuracy, in three ways. First, we investigate multiple approaches to clustering, including not only different clustering algorithms, but also the way time series are represented and how distance between time series is defined. We …

Validating Deep-Learning Weather Forecast Models on Recent High-Impact Extreme Events
Olivier C. Pasche, Jonathan Wider, Zhongwei Zhang, Jakob Zscheischler, Sebastian Engelke
https://arxiv.org/abs/2404.17652

Validating Deep-Learning Weather Forecast Models on Recent High-Impact Extreme Events
The forecast accuracy of deep-learning-based weather prediction models is improving rapidly, leading many to speak of a "second revolution in weather forecasting". With numerous methods being developed, and limited physical guarantees offered by deep-learning models, there is a critical need for comprehensive evaluation of these emerging techniques. While this need has been partly fulfilled by benchmark datasets, they provide little information on rare and impactful extreme events, or on compou…

Can we predict QPP? An approach based on multivariate outliers
Adrian-Gabriel Chifu, S\'ebastien D\'ejean, Moncef Garouani, Josiane Mothe, Di\'ego Ortiz, Md Zia Ullah
https://arxiv.org/abs/2402.16875

Can we predict QPP? An approach based on multivariate outliers
Query performance prediction (QPP) aims to forecast the effectiveness of a search engine across a range of queries and documents. While state-of-the-art predictors offer a certain level of precision, their accuracy is not flawless. Prior research has recognized the challenges inherent in QPP but often lacks a thorough qualitative analysis. In this paper, we delve into QPP by examining the factors that influence the predictability of query performance accuracy. We propose the working hypothesis …

LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models
Mingxing Peng (Frank), Xusen Guo (Frank), Xianda Chen (Frank), Meixin Zhu (Frank), Kehua Chen (Frank), Hao (Frank), Yang, Xuesong Wang, Yinhai Wang
https://arxiv.org/abs/2403.18344

LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models
To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict the lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this paper, we address these challenges by proposing LC-LLM, an explainable lane change prediction model that leverages the st…

Validating Deep-Learning Weather Forecast Models on Recent High-Impact Extreme Events
Olivier C. Pasche, Jonathan Wider, Zhongwei Zhang, Jakob Zscheischler, Sebastian Engelke
https://arxiv.org/abs/2404.17652

Validating Deep-Learning Weather Forecast Models on Recent High-Impact Extreme Events
The forecast accuracy of deep-learning-based weather prediction models is improving rapidly, leading many to speak of a "second revolution in weather forecasting". With numerous methods being developed, and limited physical guarantees offered by deep-learning models, there is a critical need for comprehensive evaluation of these emerging techniques. While this need has been partly fulfilled by benchmark datasets, they provide little information on rare and impactful extreme events, or on compou…

Impact of data usage for forecasting on performance of model predictive control in buildings with smart energy storage
Max Langtry, Vijja Wichitwechkarn, Rebecca Ward, Chaoqun Zhuang, Monika J. Kreitmair, Nikolas Makasis, Zack Xuereb Conti, Ruchi Choudhary
https://arxiv.org/abs/2402.12539

Impact of data usage for forecasting on performance of model predictive control in buildings with smart energy storage
Data is required to develop forecasting models for use in Model Predictive Control (MPC) schemes in building energy systems. However, data usage incurs costs from both its collection and exploitation. Determining cost optimal data usage requires understanding of the forecast accuracy and resulting MPC operational performance it enables. This study investigates the performance of both simple and state-of-the-art machine learning prediction models for MPC in a multi-building energy system simulat…

SuperdropNet: a Stable and Accurate Machine Learning Proxy for Droplet-based Cloud Microphysics
Shivani Sharma, David Greenberg
https://arxiv.org/abs/2402.18354

SuperdropNet: a Stable and Accurate Machine Learning Proxy for Droplet-based Cloud Microphysics
Cloud microphysics has important consequences for climate and weather phenomena, and inaccurate representations can limit forecast accuracy. While atmospheric models increasingly resolve storms and clouds, the accuracy of the underlying microphysics remains limited by computationally expedient bulk moment schemes based on simplifying assumptions. Droplet-based Lagrangian schemes are more accurate but are underutilized due to their large computational overhead. Machine learning (ML) based scheme…

Forecasting with panel data: Estimation uncertainty versus parameter heterogeneity
M. Hashem Pesaran, Andreas Pick, Allan Timmermann
https://arxiv.org/abs/2404.11198

Forecasting with panel data: Estimation uncertainty versus parameter heterogeneity
We provide a comprehensive examination of the predictive accuracy of panel forecasting methods based on individual, pooling, fixed effects, and Bayesian estimation, and propose optimal weights for forecast combination schemes. We consider linear panel data models, allowing for weakly exogenous regressors and correlated heterogeneity. We quantify the gains from exploiting panel data and demonstrate how forecasting performance depends on the degree of parameter heterogeneity, whether such heterog…

The Jive Verification System and its Transformative Impact on Weather Forecasting Operations
Nicholas Loveday, Deryn Griffiths, Tennessee Leeuwenburg, Robert Taggart, Thomas C. Pagano, George Cheng, Kevin Plastow, Elizabeth Ebert, Cassandra Templeton, Maree Carroll, Mohammadreza Khanarmuei, Isha Nagpal
https://arxiv.org/abs/2404…

The Jive Verification System and its Transformative Impact on Weather Forecasting Operations
Forecast verification is critical for continuous improvement in meteorological organizations. The Jive verification system was originally developed to assess the accuracy of public weather forecasts issued by the Australian Bureau of Meteorology. It started as a research project in 2015 and gradually evolved into the operational verification system that went live in 2022. The system includes daily verification dashboards for forecasters to visualize recent forecast performance and "Evidence Tar…

This https://arxiv.org/abs/2401.16512 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Limits to extreme event forecasting in chaotic systems
Predicting extreme events in chaotic systems, characterized by rare but intensely fluctuating properties, is of great importance due to their impact on the performance and reliability of a wide range of systems. Some examples include weather forecasting, traffic management, power grid operations, and financial market analysis, to name a few. Methods of increasing sophistication have been developed to forecast events in these systems. However, the boundaries that define the maximum accuracy of f…

The Jive Verification System and its Transformative Impact on Weather Forecasting Operations
Nicholas Loveday, Deryn Griffiths, Tennessee Leeuwenburg, Robert Taggart, Thomas C. Pagano, George Cheng, Kevin Plastow, Elizabeth Ebert, Cassandra Templeton, Maree Carroll, Mohammadreza Khanarmuei, Isha Nagpal
https://arxiv.org/abs/2404…

The Jive Verification System and its Transformative Impact on Weather Forecasting Operations
Forecast verification is critical for continuous improvement in meteorological organizations. The Jive verification system was originally developed to assess the accuracy of public weather forecasts issued by the Australian Bureau of Meteorology. It started as a research project in 2015 and gradually evolved into the operational verification system that went live in 2022. The system includes daily verification dashboards for forecasters to visualize recent forecast performance and "Evidence Tar…

This https://arxiv.org/abs/2404.07396 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

ChatGPT Can Predict the Future when it Tells Stories Set in the Future About the Past
This study investigates whether OpenAI's ChatGPT-3.5 and ChatGPT-4 can accurately forecast future events using two distinct prompting strategies. To evaluate the accuracy of the predictions, we take advantage of the fact that the training data at the time of experiment stopped at September 2021, and ask about events that happened in 2022 using ChatGPT-3.5 and ChatGPT-4. We employed two prompting strategies: direct prediction and what we call future narratives which ask ChatGPT to tell fictional…

This https://arxiv.org/abs/2401.16512 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Limits to extreme event forecasting in chaotic systems
Predicting extreme events in chaotic systems, characterized by rare but intensely fluctuating properties, is of great importance due to their impact on the performance and reliability of a wide range of systems. Some examples include weather forecasting, traffic management, power grid operations, and financial market analysis, to name a few. Methods of increasing sophistication have been developed to forecast events in these systems. However, the boundaries that define the maximum accuracy of f…