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on Forecasting |
| By: | Yannik Pflugfelder; Aiko Schinke-Nendza; Jonathan Dumas; Christoph Weber (Chair for Management Sciences and Energy Economics, University of Duisburg-Essen) |
| Abstract: | Accurate forecasting of solar PV generation is critical for integrating renewable energy into power systems. This paper presents a multivariate probabilistic forecasting model that addresses the challenges posed by imbalanced data resulting from day and night-time periods in solar photovoltaic (PV) generation. The proposed approach offers a robust and accurate method for predicting solar PV output by incorporating forecast updates and modeling the temporal interdependencies. The methodology is applied to a case study in France, demonstrating effectiveness across different spatial granularities and forecast horizons. The model uses advanced data handling methods combined with copula models, resulting in improved Energy Scores and Variogram-based Scores. These improvements underscore the importance of addressing imbalanced data and utilizing multivariate models with repeated updates to enhance solar forecasting accuracy. This work contributes to advancing forecasting techniques essential for integrating renewable energy into power grids, supporting the global transition to a sustainable energy future. |
| Keywords: | Multivariate probabilistic forecasts, Forecast updates, Solar generation, Copula |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:dui:wpaper:2407 |
| By: | Mabsur Fatin Bin Hossain; Lubna Zahan Lamia; Md Mahmudur Rahman; Md Mosaddek Khan |
| Abstract: | Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.12748 |
| By: | Hanwool Lee; Heehwan Park |
| Abstract: | This paper presents a new approach to volume ratio prediction in financial markets, specifically targeting the execution of Volume-Weighted Average Price (VWAP) strategies. Recognizing the importance of accurate volume profile forecasting, our research leverages the Transformer architecture to predict intraday volume ratio at a one-minute scale. We diverge from prior models that use log-transformed volume or turnover rates, instead opting for a prediction model that accounts for the intraday volume ratio's high variability, stabilized via log-normal transformation. Our input data incorporates not only the statistical properties of volume but also external volume-related features, absolute time information, and stock-specific characteristics to enhance prediction accuracy. The model structure includes an encoder-decoder Transformer architecture with a distribution head for greedy sampling, optimizing performance on high-liquidity stocks across both Korean and American markets. We extend the capabilities of our model beyond point prediction by introducing probabilistic forecasting that captures the mean and standard deviation of volume ratios, enabling the anticipation of significant intraday volume spikes. Furthermore, an agent with a simple trading logic demonstrates the practical application of our model through live trading tests in the Korean market, outperforming VWAP benchmarks over a period of two and a half months. Our findings underscore the potential of Transformer-based probabilistic models for volume ratio prediction and pave the way for future research advancements in this domain. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.10956 |
| By: | Jue Xiao; Tingting Deng; Shuochen Bi |
| Abstract: | In recent fast-paced financial markets, investors constantly seek ways to gain an edge and make informed decisions. Although achieving perfect accuracy in stock price predictions remains elusive, artificial intelligence (AI) advancements have significantly enhanced our ability to analyze historical data and identify potential trends. This paper takes AI driven stock price trend prediction as the core research, makes a model training data set of famous Tesla cars from 2015 to 2024, and compares LSTM, GRU, and Transformer Models. The analysis is more consistent with the model of stock trend prediction, and the experimental results show that the accuracy of the LSTM model is 94%. These methods ultimately allow investors to make more informed decisions and gain a clearer insight into market behaviors. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05790 |