nep-for New Economics Papers
on Forecasting
Issue of 2023‒06‒26
three papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting the Confirmed COVID-19 Cases Using Modal Regression By XIN JING; JIN SEO CHO
  2. Machine Learning and Deep Learning Forecasts of Electricity Imbalance Prices By Sinan Deng; John Inekwe; Vladimir Smirnov; Andrew Wait; Chao Wang
  3. Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels By Jungyeon Park; Est\^ev\~ao Alvarenga; Jooyoung Jeon; Ran Li; Fotios Petropoulos; Hokyun Kim; Kwangwon Ahn

  1. By: XIN JING (Yonsei University); JIN SEO CHO (Yonsei University)
    Abstract: This study utilizes modal regression to forecast the cumulative confirmed COVID-19 cases in Canada, Japan, South Korea, and the United States. The objective is to improve the accuracy of the forecasts compared to standard mean and median regressions. To evaluate the performance of the forecasts, we conduct simulations and introduce a metric called the coverage quantile function (CQF), which is optimized using modal regression. By applying modal regression to popular time-series models for COVID19 data, we provide empirical evidence that the forecasts generated by the modal regression outperform those produced by the mean and median regressions in terms of the CQF. This finding addresses the limitations of the mean and median regression forecasts.
    Keywords: Forecasting COVID-19 cases; Modal regression; Conditional mode; MEM algorithm; Density estimation.
    JEL: C22 C53 I18
    Date: 2023–06
  2. By: Sinan Deng; John Inekwe; Vladimir Smirnov; Andrew Wait; Chao Wang
    Abstract: In this paper, we propose a seasonal attention mechanism, the effectiveness of which is evaluated via the Bidirectional Long Short-Term Memory (BiLSTM) model. We compare its performance with alternative deep learning and machine learning models in forecasting the balancing settlement prices in the electricity market of Great Britain. Critically, the Seasonal Attention-Based BiLSTM framework provides a superior forecast of extreme prices with an out-of-sample gain in the predictability of 25-37% compared with models in the literature. Our forecasting techniques could aid both market participants, to better manage their risk and assign their assets, and policy makers, to operate the system at lower cost.
    Keywords: forecasting; electricity; balance settlement prices; Long Short-Term Memory; machine learning.
    Date: 2023–06
  3. By: Jungyeon Park; Est\^ev\~ao Alvarenga; Jooyoung Jeon; Ran Li; Fotios Petropoulos; Hokyun Kim; Kwangwon Ahn
    Abstract: In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants' strategies. Accurate probabilistic forecasts at low aggregation levels can improve peer-to-peer energy sharing, demand response, and the operation of reliable distribution networks. However, these applications require not only probabilistic demand forecasts, which involve quantification of the forecast uncertainty, but also determining which consumers to include in the aggregation to meet electricity supply at the forecast lead time. While research papers have been proposed on the supply side, no similar research has been conducted on the demand side. This paper presents a method for creating a portfolio that optimally aggregates demand for a given energy demand, minimizing forecast inaccuracy of overall low-level aggregation. Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households' demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity. An evaluation of probabilistic load forecasts demonstrates that all three approaches enhance the accuracy of forecasts produced by random portfolios, with the Seasonal Residual approach for Korea and Ireland outperforming the others in terms of both accuracy and computational efficiency.
    Date: 2023–04

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