Abstract
Time-series forecasting is a fundamental problem associated with a wide range of engineering, financial, and social applications. The challenge arises from the complexity due to the time-variant property of time series and the inevitable diminishing utility of predictive models. Therefore, it is generally difficult to accurately predict values, especially in a multi-step ahead setting. However, in domains such as financial time series forecasting, an ex-ante prediction of the relative order of values in the near future is sufficient; i.e., the next 100 days can help make meaningful investment decisions. In this paper, we propose a dynamic prediction framework that makes it possible to make an ex-ante forecast of time series with a special focus on the relative ordering of the forecast within a forward-looking time horizon. Through the lens of the concordance index (CI), we compare the proposed method with conventional regression-based time-series forecasting methods, discriminative learning methods and hybrid methods. Moreover, we discuss the use of the proposed framework for different types of time series and under a variety of conditions. Extensive experimental results on financial time series across a majority of liquid asset classes show that the proposed framework outperforms the benchmark methods significantly.
Highlights
Time-series forecasting is a fundamental problem associated with a wide range of science, engineering, finance, and societal issues
It is evaluated based on the nowcasting performance, which reduces to certain evaluation metrics such as the tracking error
In consideration of the fact that volatility is a critical facet of financial time series and the fact that the volatility scaling technique plays a significant role in the construction of investment strategies [59], we adopt the risk-adjusted momentum features FrollingReturn(t, i) by FrollingReturn(t, i; x[0:t])
Summary
Time-series forecasting is a fundamental problem associated with a wide range of science, engineering, finance, and societal issues. In the financial and social domains, the impact of time-series forecasting goes beyond nowcasting and it shifts its focus from the near future to the long-term horizon, bringing in other perspectives such as concordance, causality, in order to guide the decision makers to intervene appropriately In this case, the use of a forecasted time series is prioritized over the conventional tracking error. The majority of research work in time-series forecasting focuses on shortterm forecasting, and often even on one-step ahead setting For this reason, the potential variations in optimization objectives and evaluation metrics are not well explored beyond a predominant focus on tracking error. The actual utility of the forecast in a one-step ahead setting varies by application, the corresponding evaluation metric is often monotonously inherited from that of regression-based methods In such scenarios, the common determinant criterion is the tracking error calculated from the point-wise difference between the ground truth and the forecasted value.
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