On the Design and use of Ensembles of Multi-model Simulations for Forecasting

Probabilistic temperature forecasting with statistical calibration in Hungary

Weather forecasting is mostly based on the outputs of deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions result in forecast ensembles which is are used for estimating the distribution of future atmospheric variables. However, these ensembles are usually under-dispersive and uncalibrated, so post-processing is required. In the present work Bayesian Model Averaging (BMA) is applied for calibrating ensembles of temperature forecasts produced by the operational Limited Area Model Ensemble Prediction System of the Hungarian Meteorolog…

Probabilistic wind speed forecasting in Hungary

Prediction of various weather quantities is mostly based on deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions result ensembles of forecasts which are applied for estimating the distribution of future weather quantities. However, the ensembles are usually under-dispersive and uncalibrated, so post-processing is required. In the present work Bayesian Model Averaging (BMA) is applied for calibrating ensembles of wind speed forecasts produced by the operational Limited Area Model Ensemble Prediction System of the Hungarian Meteorolo…

Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories

Recent studies concerning the point electricity price forecasting have shown evidence that the hourly German Intraday Continuous Market is weak-form efficient. Therefore, we take a novel, advanced approach to the problem. A probabilistic forecasting of the hourly intraday electricity prices is performed by simulating trajectories in every trading window to receive a realistic ensemble to allow for more efficient intraday trading and redispatch. A generalized additive model is fitted to the price differences with the assumption that they follow a zero-inflated distribution, precisely a mixture …

Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature

Forecast ensembles are typically employed to account for prediction uncertainties in numerical weather prediction models. However, ensembles often exhibit biases and dispersion errors, thus they require statistical post-processing to improve their predictive performance. Two popular univariate post-processing models are the Bayesian model averaging (BMA) and the ensemble model output statistics (EMOS). In the last few years increased interest has emerged in developing multivariate post-processing models, incorporating dependencies between weather quantities, such as for example a bivariate dis…

Comparison of Combination Methods to Create Calibrated Ensemble Forecasts for Seasonal Influenza in the U.S

The characteristics of influenza seasons varies substantially from year to year, posing challenges for public health preparation and response. Influenza forecasting is used to inform seasonal outbreak response, which can in turn potentially reduce the societal impact of an epidemic. The United States Centers for Disease Control and Prevention, in collaboration with external researchers, has run an annual prospective influenza forecasting exercise, known as the FluSight challenge. A subset of participating teams has worked together to produce a collaborative multi-model ensemble, the FluSight N…

集成直接多步预测方法及其在宏观经济和金融数据上的比较研究

Ensembled Direct Multi-Step forecasting methodology with comparison on macroeconomic and financial data

提出集成直接多步（EDMS）预测方法，结合集成与直接多步技术，在宏观经济和金融数据上平均提升预测性能33.32%，有时超过60%。

Two-Step Meta-Learning for Time-Series Forecasting Ensemble

Amounts of historical data collected increase and business intelligence applicability with automatic forecasting of time series are in high demand. While no single time series modeling method is universal to all types of dynamics, forecasting using an ensemble of several methods is often seen as a compromise. Instead of fixing ensemble diversity and size, we propose to predict these aspects adaptively using meta-learning. Meta-learning here considers two separate random forest regression models, built on 390 time-series features, to rank 22 univariate forecasting methods and recommend ensemble…

Multivariate discrete copulas, with applications in probabilistic weather forecasting

In probability and statistics, copulas play important roles theoretically as well as to address a wide range of problems in various application areas. We introduce the concept of multivariate discrete copulas, discuss their equivalence to stochastic arrays, and prove a multivariate discrete version of Sklar's theorem. These results provide the theoretical frame for multivariate statistical methods to postprocess weather forecasts made by ensemble systems, including the ensemble copula coupling approach and the Schaake shuffle.

Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging

Ensembles of forecasts are typically employed to account for the forecast uncertainties inherent in predictions of future weather states. However, biases and dispersion errors often present in forecast ensembles require statistical post-processing. Univariate post-processing models such as Bayesian model averaging (BMA) have been successfully applied for various weather quantities. Nonetheless, BMA and many other standard post-processing procedures are designed for a single weather variable, thus ignoring possible dependencies among weather quantities. In line with recently upcoming research t…

一种新颖的分解-集成时间序列预测框架：捕捉潜在波动率信息

A novel decomposed-ensemble time series forecasting framework: capturing underlying volatility information

本文提出一种结合Variational Mode Decomposition分解与GARCH波动率提取的分解-集成时间序列预测框架，利用神经网络融合数值与波动率信息，显著降低了MSE、RMSE和MAPE。

保险损失准备金的集成分布预测

Ensemble distributional forecasting for insurance loss reserving

本文提出一个系统性框架以客观集成多个随机损失准备金模型，通过考虑完整分布特性和准备金数据固有特征进行优化组合，在合成数据集中表现优于传统模型选择与等权集成方法。