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언어부터 이미지, 오픈 웨이트부터 API까지. 기능과 실제 비용을 비교하세요.
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AI 모델 탐색 4

제공사 입력 출력 라이선스

주목할 모델을 찾아보세요

autoformer-tourism-monthly

huggingface

The Autoformer model was proposed in Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting by Haixu Wu, Jiehui Xu, Jianmin Wang and Mingsheng Long. *Extending the forecasting time is a critical demand for real applications, such as extreme weather early warning and long-term energy consumption planning. This paper studies the long-term forecasting problem of time series. Prior Transformer-based models adopt various self-attention mechanisms to discover the long-range dependencies. However, intricate temporal patterns of the long-term future prohibit the model from finding reliable dependencies. Also, Transformers have to adopt the sparse versions of point-wise self-attentions for long series efficiency, resulting in the information utilization bottleneck. Going beyond Transformers, we design Autoformer as a novel decomposition architecture with an Auto-Correlation mechanism. We break with the pre-processing convention of series decomposition and renovate it as a basic inner block of deep models. This design empowers Autoformer with progressive decomposition capacities for complex time series. Further, inspired by the stochastic process theory, we design the Auto-Correlation mechanism based on the series periodicity, which conducts the dependencies discovery and representation aggregation at the sub-series level. Auto-Correlation outperforms self-attention in both efficiency and accuracy. In long-term forecasting, Autoformer yields state-of-the-art accuracy, with a 38% relative improvement on six benchmarks, covering five practical applications: energy, traffic, economics, weather and disease.*

번역 대기 중 · 원문 표시

분류 준비 중

입력 미확인→ 출력 미확인

요금 미확인멀티모달 및 채널 요금 별도 확인

자세히 보기 ↗

비교 추가출처 / 문서 ↗

CodeBERTa-small-v1

huggingface

CodeBERTa is a RoBERTa-like model trained on the CodeSearchNet dataset from GitHub. The **tokenizer** is a Byte-level BPE tokenizer trained on the corpus using Hugging Face `tokenizers`. Because it is trained on a corpus of code (vs. natural language), it encodes the corpus efficiently (the sequences are between 33% to 50% shorter, compared to the same corpus tokenized by gpt2/roberta).

번역 대기 중 · 원문 표시

빈칸 및 마스크

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

자세히 보기 ↗

비교 추가출처 / 문서 ↗

informer-tourism-monthly

huggingface

The Informer model was proposed in Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang. This method introduces a Probabilistic Attention mechanism to select the "active" queries rather than the "lazy" queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention. *Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a high prediction capacity of the model, which is the ability to capture precise long-range dependency coupling between output and input efficiently. Recent studies have shown the potential of Transformer to increase the prediction capacity. However, there are several severe issues with Transformer that prevent it from being directly applicable to LSTF, including quadratic time complexity, high memory usage, and inherent limitation of the encoder-decoder architecture. To address these issues, we design an efficient transformer-based model for LSTF, named Informer, with three distinctive characteristics: (i) a ProbSparse self-attention mechanism, which achieves O(L logL) in time complexity and memory usage, and has comparable performance on sequences' dependency alignment. (ii) the self-attention distilling highlights dominating attention by halving cascading layer input, and efficiently handles extreme long input sequences. (iii) the generative style decoder, while conceptually simple, predicts the long time-series sequences at one forward operation rather than a step-by-step way, which drastically improves the inference speed of long-sequence predictions. Extensive experiments on four large-scale datasets demonstrate that Informer significantly outperforms existing methods and provides a new solution to the LSTF problem.*

번역 대기 중 · 원문 표시

분류 준비 중

입력 미확인→ 출력 미확인

요금 미확인멀티모달 및 채널 요금 별도 확인

자세히 보기 ↗

비교 추가출처 / 문서 ↗

time-series-transformer-tourism-monthly

huggingface

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

표 및 시계열 예측

입력 미확인→ 출력 미확인

요금 미확인멀티모달 및 채널 요금 별도 확인

자세히 보기 ↗

비교 추가출처 / 문서 ↗

COST EXPLORER

좋은 모델, 명확한 비용.

비용 분석

월간 토큰 사용량으로 예상 API 비용을 비교하세요.

월간 입력 토큰 10 M월간 출력 토큰 2 M1 M = 100만 토큰 · USD
동일 채널의 기본 텍스트 토큰 요금 비교

수집된 백만 토큰당 USD 요금입니다. 캐시 할인, 구간 요금, 추가 추론 출력, 세금은 제외됩니다. 최신 요금은 출처에서 확인하세요.

MODEL HANDBOOK

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선택 가이드 및 FAQ

API 요금은 어떻게 비교하나요?+

입력, 출력, 캐시 요금을 구분하고 동일 작업량으로 비교하세요. 이미지, 음성, 영상은 개수, 시간, 해상도로 과금될 수 있습니다.

오픈 웨이트는 무료 상업 이용을 뜻하나요?+

반드시 그렇지는 않습니다. 버전별 라이선스, 상업 이용 제한, 정책을 확인하세요. 자체 호스팅에는 연산 및 유지 비용도 듭니다.

1위 모델이 나에게 가장 적합한가요?+

순위는 작업, 버전, 평가 방식에 따라 다릅니다. 실제 업무로 정확도, 지연, 안정성, 비용을 비교하세요.

나에게 맞는 AI 모델을 찾아보세요.

AI 모델 탐색 4

autoformer-tourism-monthly

CodeBERTa-small-v1

informer-tourism-monthly

time-series-transformer-tourism-monthly

일치하는 모델 없음

좋은 모델, 명확한 비용.

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