Effective stock price forecasting (estimating future prices) and prediction
(estimating future price changes) are pivotal for investors, regulatory
agencies, and policymakers. These tasks enable informed decision-making, risk
management, strategic planning, and superior portfolio returns. Despite their
importance, forecasting and prediction are challenging due to the dynamic
nature of stock price data, which exhibit significant temporal variations in
distribution and statistical properties. Inoltre, while both forecasting
and prediction targets are derived from the same dataset, their statistical
characteristics differ significantly. Forecasting targets typically follow a
log-normal distribution, characterized by significant shifts in mean and
variance over time, whereas prediction targets adhere to a normal distribution.
Inoltre, although multi-step forecasting and prediction offer a broader
perspective and richer information compared to single-step approaches, it is
much more challenging due to factors such as cumulative errors and long-term
temporal variance. As a result, many previous works have tackled either
single-step stock price forecasting or prediction instead. To address these
issues, we introduce a novel model, termed Patched Channel Integration Encoder
(PCIE), to tackle both stock price forecasting and prediction. In this model,
we utilize multiple stock channels that cover both historical prices and price
changes, and design a novel tokenization method to effectively embed these
channels in a cross-channel and temporally efficient manner. Nello specifico, IL
tokenization process involves univariate patching and temporal learning with a
channel-mixing encoder to reduce cumulative errors. Comprehensive experiments
validate that PCIE outperforms current state-of-the-art models in forecast and
prediction tasks.

Questo articolo esplora i giri e le loro implicazioni.

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