March 14, 2024, 4:43 a.m. | Hyunwook Lee, Chunggi Lee, Hongkyu Lim, Sungahn Ko

cs.LG updates on

arXiv:2210.15050v2 Announce Type: replace
Abstract: Time-series forecasting has gained increasing attention in the field of artificial intelligence due to its potential to address real-world problems across various domains, including energy, weather, traffic, and economy. While time-series forecasting is a well-researched field, predicting complex temporal patterns such as sudden changes in sequential data still poses a challenge with current models. This difficulty stems from minimizing Lp norm distances as loss functions, such as mean absolute error (MAE) or mean square error …

abstract artificial artificial intelligence arxiv attention cs.lg domains economy energy forecasting function intelligence loss patterns series temporal traffic transformation type weather world

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