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on Forecasting |
| By: | Katarzyna Maciejowska; Weronika Nitka |
| Abstract: | In this article, a multiple split method is proposed that enables construction of multidimensional probabilistic forecasts of a selected set of variables. The method uses repeated resampling to estimate uncertainty of simultaneous multivariate predictions. This nonparametric approach links the gap between point and probabilistic predictions and can be combined with different point forecasting methods. The performance of the method is evaluated with data describing the German short-term electricity market. The results show that the proposed approach provides highly accurate predictions. The gains from multidimensional forecasting are the largest when functions of variables, such as price spread or residual load, are considered. Finally, the method is used to support a decision process of a moderate generation utility that produces electricity from wind energy and sells it on either a day-ahead or an intraday market. The company makes decisions under high uncertainty because it knows neither the future production level nor the prices. We show that joint forecasting of both market prices and fundamentals can be used to predict the distribution of a profit, and hence helps to design a strategy that balances a level of income and a trading risk. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.07795 |
| By: | Adam Bahelka; Harmen de Weerd |
| Abstract: | Inflation is one of the most important economic indicators closely watched by both public institutions and private agents. This study compares the performance of a traditional econometric model, Mixed Data Sampling regression, with one of the newest developments from the field of Artificial Intelligence, a foundational time series forecasting model based on a Long short-term memory neural network called Lag-Llama, in their ability to nowcast the Harmonized Index of Consumer Prices in the Euro area. Two models were compared and assessed whether the Lag-Llama can outperform the MIDAS regression, ensuring that the MIDAS regression is evaluated under the best-case scenario using a dataset spanning from 2010 to 2022. The following metrics were used to evaluate the models: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), correlation with the target, R-squared and adjusted R-squared. The results show better performance of the pre-trained Lag-Llama across all metrics. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.08510 |
| By: | Monika Zimmermann; Florian Ziel |
| Abstract: | Accurate forecasts of the impact of spatial weather and pan-European socio-economic and political risks on hourly electricity demand for the mid-term horizon are crucial for strategic decision-making amidst the inherent uncertainty. Most importantly, these forecasts are essential for the operational management of power plants, ensuring supply security and grid stability, and in guiding energy trading and investment decisions. The primary challenge for this forecasting task lies in disentangling the multifaceted drivers of load, which include national deterministic (daily, weekly, annual, and holiday patterns) and national stochastic weather and autoregressive effects. Additionally, transnational stochastic socio-economic and political effects add further complexity, in particular, due to their non-stationarity. To address this challenge, we present an interpretable probabilistic mid-term forecasting model for the hourly load that captures, besides all deterministic effects, the various uncertainties in load. This model recognizes transnational dependencies across 24 European countries, with multivariate modeled socio-economic and political states and cross-country dependent forecasting. Built from interpretable Generalized Additive Models (GAMs), the model enables an analysis of the transmission of each incorporated effect to the hour-specific load. Our findings highlight the vulnerability of countries reliant on electric heating under extreme weather scenarios. This emphasizes the need for high-resolution forecasting of weather effects on pan-European electricity consumption especially in anticipation of widespread electric heating adoption. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.00507 |
| By: | Mingshu Li; Bhaskarjit Sarmah; Dhruv Desai; Joshua Rosaler; Snigdha Bhagat; Philip Sommer; Dhagash Mehta |
| Abstract: | Due to the dynamic nature of financial markets, maintaining models that produce precise predictions over time is difficult. Often the goal isn't just point prediction but determining uncertainty. Quantifying uncertainty, especially the aleatoric uncertainty due to the unpredictable nature of market drivers, helps investors understand varying risk levels. Recently, quantile regression forests (QRF) have emerged as a promising solution: Unlike most basic quantile regression methods that need separate models for each quantile, quantile regression forests estimate the entire conditional distribution of the target variable with a single model, while retaining all the salient features of a typical random forest. We introduce a novel approach to compute quantile regressions from random forests that leverages the proximity (i.e., distance metric) learned by the model and infers the conditional distribution of the target variable. We evaluate the proposed methodology using publicly available datasets and then apply it towards the problem of forecasting the average daily volume of corporate bonds. We show that using quantile regression using Random Forest proximities demonstrates superior performance in approximating conditional target distributions and prediction intervals to the original version of QRF. We also demonstrate that the proposed framework is significantly more computationally efficient than traditional approaches to quantile regressions. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.02355 |