Unverified50% confidenceFactExact time
波动率具有长记忆特性，其自相关函数衰减极为缓慢，Hurst指数通常在0.7-0.9之间，远高于随机游走的0.5
1
Sources
50%
Confidence
Long-term
Relevance
8/12/2026
First Seen
Sources
金融机器学习模型为何难以评估？核心挑战与避坑指南
Related Claims
VerifiedThe Ebbinghaus forgetting curve reveals that memory strength decays exponentially over time, and this process can be slowed through spaced repetition.72% similarUnverified只有在LLM推理比embedding加向量检索慢数十倍以上时，语义缓存才能同时实现降本与提速66% similarUnverified从信息论角度看，LLM的输出熵相对较低，倾向于收敛到训练数据的高概率区域65% similarUnverifiedAn intermittently faulty memory module may alternately produce correct and incorrect computation results, causing sporadic deviations in gradient calculations during neural network training, affecting model convergence without triggering hardware alerts.64% similarUnverified减少重复索引可使智能体的冷启动时间从数十秒缩短到几秒，提升响应速度64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/732597API
curl https://kongchang.com/api/v1/knowledge/claims/732597MCP
get_claim(id=732597)