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Full-Text Articles in Physical Sciences and Mathematics
Robust Median Reversion Strategy For Online Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Hoi, Steven C. H., Shuigeng Zhou
Robust Median Reversion Strategy For Online Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Hoi, Steven C. H., Shuigeng Zhou
Research Collection School Of Computing and Information Systems
On-line portfolio selection has been attracting increasing interests from artificial intelligence community in recent decades. Mean reversion, as one most frequent pattern in financial markets, plays an important role in some state-of-the-art strategies. Though successful in certain datasets, existing mean reversion strategies do not fully consider noises and outliers in the data, leading to estimation error and thus non-optimal portfolios, which results in poor performance in practice. To overcome the limitation, we propose to exploit the reversion phenomenon by robust L1-median estimator, and design a novel on-line portfolio selection strategy named "Robust Median Reversion" (RMR), which makes optimal portfolios based …
Online Arima Algorithms For Time Series Prediction, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun
Online Arima Algorithms For Time Series Prediction, Chenghao Liu, Hoi, Steven C. H., Peilin Zhao, Jianling Sun
Research Collection School Of Computing and Information Systems
Autoregressive integrated moving average (ARIMA) is one of the most popular linear models for time series forecasting due to its nice statistical properties and great flexibility. However, its parameters are estimated in a batch manner and its noise terms are often assumed to be strictly bounded, which restricts its applications and makes it inefficient for handling large-scale real data. In this paper, we propose online learning algorithms for estimating ARIMA models under relaxed assumptions on the noise terms, which is suitable to a wider range of applications and enjoys high computational efficiency. The idea of our ARIMA method is to …