CRAFT: Time Series Forecasting with Cross-Future Behavior Awareness
Yingwei Zhang, Ke Bu, Zhuoran Zhuang, Tao Xie, Yao Yu, Dong Li, Yang Guo, Detao Lv
TL;DR
This paper introduces CRAFT, a novel time series forecasting method that leverages cross-future behavior trends and multiple modules to improve prediction accuracy, validated through extensive experiments on real-world data.
Contribution
The paper proposes CRAFT, a new TSF approach that incorporates cross-future behavior analysis and hierarchical trend extraction modules for enhanced forecasting performance.
Findings
CRAFT outperforms existing methods on real-world datasets.
The hierarchical trend modules effectively capture complex time series patterns.
The demand-constrained loss improves prediction calibration.
Abstract
The past decades witness the significant advancements in time series forecasting (TSF) across various real-world domains, including e-commerce and disease spread prediction. However, TSF is usually constrained by the uncertainty dilemma of predicting future data with limited past observations. To settle this question, we explore the use of Cross-Future Behavior (CFB) in TSF, which occurs before the current time but takes effect in the future. We leverage CFB features and propose the CRoss-Future Behavior Awareness based Time Series Forecasting method (CRAFT). The core idea of CRAFT is to utilize the trend of cross-future behavior to mine the trend of time series data to be predicted. Specifically, to settle the sparse and partial flaws of cross-future behavior, CRAFT employs the Koopman Predictor Module to extract the key trend and the Internal Trend Mining Module to supplement the…
Peer Reviews
Decision·Submitted to ICLR 2025
CRAFT is an innovative time series forecasting method that enhances predictive model performance by defining and leveraging Cross-Future Behavior (CFB). It comprises three main modules to address the sparsity and partiality of CFB, as well as to acquire representative trends from higher levels, and calibrates the distribution deviation of forecast results with a demand-constrained loss. CRAFT has demonstrated exceptional performance on real-world datasets, significantly improving prediction accu
The research on CRAFT, while presenting a significant advancement in time series forecasting, does have certain limitations that can be discussed in terms of real-world data complexity, redundancy, interpretability, and data transferability: 1. **Real-World Data Complexity and Variability:** - Real-world data is often characterized by noise, outliers, and non-stationarity, which can affect the model's ability to learn accurate patterns. CRAFT may struggle to capture these complex dynamics, e
S1. The definition of CFB expands the traditional understanding of time series features by including elements that are observable in advance but affect future outcomes. S2. This work is evident in its robust methodology and empirical validation. The authors employ a well-structured framework composed of three distinct modules—KPM, ITM, and ETG—each addressing specific challenges associated with CFB and time series forecasting. S3. This work's demonstrated improvement in forecasting accuracy
W1. First of all, while the introduction of CFB is a strong point, the paper could benefit from a broader exploration of its implications and applications. The current formulation primarily focused on e-commerce and hotel booking scenarios. W2. The experimental section could be improved in several ways. For example, conducting longitudinal studies to evaluate how CRAFT performs over extended periods or under different market conditions would add depth to the findings. W3. - A systematic sen
1. The field of time series forecasting that this paper focuses on is worth studying. 2. The structure of the article is relatively complete.
1. The research motivation has significant issues. Although there are instances in e-commerce where actions are taken at a past moment for a future one, it is evident that they are entirely corresponding. Changes in final labels can be understood as minor variations based on the booking situation. Therefore, even though the actual future events have not occurred at the current time, crucial actions that influence the future have already taken place in history. Introducing the so-called CFB data
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Taxonomy
TopicsTime Series Analysis and Forecasting · Complex Systems and Time Series Analysis