In this article, I review the research in the 2024 NeurIPS conference paper, From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection, by Wang et al. The paper explores integrating event analysis into LLM-based time-series forecasting models. Real-time text such as news articles or social media posts are underutilized in…
This study explores the impact of weather features on short to medium electricity load prediction across diverse geographical locations. Using hourly load data, we evaluated the effectiveness of several feature selection methods, including Mutual Information (MI), Principal Component Analysis (PCA), Lasso, and Heatmap correlation. We benchmarked these feature selection methods with a hybrid deep learning model to investigate the impact of choosing…
Transformers, powered by scaling laws, have transformed the landscape of AI, but their performance in time series forecasting is less definitive. This post explores the effectiveness of transformers, the challenges of scaling, and the rise of specialized foundation models like TTM, MOMENT, and Chronos. Do these big models live up to the hype, or are…
This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Price spikes are unexpected and abrupt extreme prices whose value can be several orders of magnitude higher than normal electricity prices. Moreover, due to their stochastic nature, price spikes are short-lived extreme price variations observed in the short-term operation of all…