Traditional methods for point forecasting in univariate random walks often
fail to surpass naive benchmarks due to data unpredictability. This study
introduces a novel forecasting method that fuses movement prediction (binary
classification) with naive forecasts for accurate one-step-ahead point
forecasting. The method's efficacy is demonstrated through theoretical
analysis, simulations, and real-world data experiments. It reliably exceeds
naive forecasts with movement prediction accuracies as low as 0.55,
outperforming baseline models like ARIMA, linear regression, MLP, and LSTM
networks in forecasting the S\&P 500 index and Bitcoin prices. This method is
particularly advantageous when accurate point predictions are challenging but
accurate movement predictions are attainable, translating movement predictions
into point forecasts in random walk contexts.

通过将运动预测与朴素预测相融合，以实现准确的一步点预测，本研究介绍了一种新颖的预测方法。通过理论分析、模拟和真实世界数据实验，证明了该方法的有效性。它可在运动预测准确率低至 0.55 的情况下，优于基准模型如 ARIMA、线性回归、MLP 和 LSTM 网络，在预测 S&P 500 指数和比特币价格方面可靠地超过朴素预测。当准确的点预测具有挑战性但能够获得准确的运动预测时，该方法尤为有利，可以将运动预测转化为随机游走环境中的点预测。