patchtst-fm-r1

모델 소개

This model card is for the non-commercial, research version of PatchTST-FM-r1. Please also check-out the Apache-2.0 licensed IBM Granite version. The architecture incorporates the following changes: - residual blocks in the input and output projections - a quantile head to support probabilistic forecasting - enhanced training strategies incorporating contiguous patch masking, and random masking in the forecast period - trained with reconstruction-loss objective - forecast at inference time is cast as "reconstruction" of the masked forecast period while past context is not masked. If the context period has missing values, both the missing timepoints and forecast timepoints are filled in --- thus providing simultaneous imputation and forecast capability. The particular model here was trained on a diverse set of data (see details below) using significantly longer context length than done previously (8192). Here the context includes both the input context as well as the length of the desired forecast horizon. Shorter input series are pre-pended with the mean of the series values in the context and the padded regions are treated as masked. The d_model is set to 1024, patch_length is 16, and we train the quantile head with 99 quantiles. In total, the model has ~260M parameters, ~250M of which are in the core transformer layers.

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

자주 묻는 질문

출처 및 추적 기록

许可证：cc-by-nc-sa-4.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。