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<!DOCTYPE html>
<html lang="en">
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<title>Timer: Large Time Series Foundation Model</title>
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<!-- ======= hero Section ======= -->
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<h2>Analyse <span>Universal</span> Time Series <br> Through <span>Large Model</span></h2>
<div>
<a href="#about" class="btn-get-started scrollto">Get Started</a>
<a href="https://arxiv.org/abs/2402.02368" class="btn-projects scrollto">Our Paper</a>
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<img src="assets/img/about-img-arch.png" alt="">
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<div class="col-lg-6 content">
<h2>Welcome to Timer - Universal Time Series Analysis</h2>
<h3>Timer originated from the <span>School of Software at Tsinghua University</span>, and was developed for the field of time series analysis by constructing large-scale time series datasets and pre training formats. By training on a <span>large dataset</span>, Timer exhibits <span>few sample generalization and multi task adaptation capabilities</span>, with considerable temporal analysis and data generation capabilities for real-world scenarios. It has the following characteristics:</h3>
<ul>
<li><i class="bi bi-check-circle"></i> Generalization: Achieving cutting-edge deep model prediction performance based on small sample fine-tuning.</li>
<li><i class="bi bi-check-circle"></i> Universality: Suitable for multiple tasks, supports variable input-output length.</li>
<li><i class="bi bi-check-circle"></i> Scalability: The model achieves improved performance as the number of parameters or pre training scale increases.</li>
</ul>
</div>
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<div class="row about-img-row">
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<img src="assets/img/about-abilities.png" alt="">
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<div class="col-lg-6 content">
<h2>Large High-quality Dataset</h2>
<h3>In pursuit of preeminent analytical prowess within the industry, we have constructed a vast dataset that harmoniously integrates <span>extensive scale, superior quality, and multifaceted domains.</span> This endeavor has endowed Timer with a large repository of high-caliber data, thereby conferring upon it a strong generalization capability across a multitude of tasks.</h3>
</div>
<div class="col-lg-6 about-img">
<img src="assets/img/about-img-data.png" alt="">
</div>
</div>
<div class="row about-img-row">
<div class="about-img">
<h2>Model Scalability</h2>
<img src="assets/img/about-scaling.png" alt="">
</div>
</div>
<div class="row" id="about-demo">
<div class="col-lg-6 about-img">
<video controls autoplay loop>
<source src="assets/img/demo.mp4">
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</video>
</div>
<div class="col-lg-6 content">
<h2>Large Model Analysis Scheme</h2>
<h3>Based on the capabilities of our model, we can construct a complete large-scale model solution. These solutions enable you to access our advanced analysis services for large models through various methods, including online API access, platform based calls, and even customized private deployment options.
<br><span>The front-end API, which is designed for online invocation, is on the cusp of being launched!</span></h3>
</div>
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</section><!-- End About Section -->
<!-- ======= Services Section ======= -->
<section id="services">
<div class="container" data-aos="fade-up">
<div class="section-header">
<h2>scenarios</h2>
<p>
Timer offers a suite of services designed to help businesses and researchers unlock the full potential of their time series data. Our Timer model is pre-trained on vast datasets and fine-tuned for specific tasks, providing a tool for applications.
</p>
</div>
<div class="row gy-4">
<div class="col-lg-6" data-aos="fade-up" data-aos-delay="100">
<div class="box">
<div class="icon"><i class="bi bi-graph-up"></i></div>
<h4 class="title"><a href="">Forecasting</a></h4>
<p class="description">The prediction modeling of time series is the core of Timer function. Our service can provide predictions for time-series data such as industrial production and natural environment.</p>
</div>
</div>
<div class="col-lg-6" data-aos="fade-up" data-aos-delay="200">
<div class="box">
<div class="icon"><i class="bi bi-bar-chart"></i></div>
<h4 class="title"><a href="">Data Imputation</a></h4>
<p class="description">Eliminate the headaches of missing data. Timer fills in the gaps, ensuring continuity and integrity in your time series datasets.</p>
</div>
</div>
<div class="col-lg-6" data-aos="fade-up" data-aos-delay="300">
<div class="box">
<div class="icon"><i class="bi bi-lightning"></i></div>
<h4 class="title"><a href="">Anomaly Detection</a></h4>
<p class="description">We provide anomaly detection capabilities based on autoregressive analysis, and Timer will provide real-time alerts for possible abnormal data.</p>
</div>
</div>
<div class="col-lg-6" data-aos="fade-up" data-aos-delay="400">
<div class="box">
<div class="icon"><i class="bi bi-upload"></i></div>
<h4 class="title"><a href="">Unified Generative Framework</a></h4>
<p class="description">Our services operate under a unified generative approach, streamlining complex tasks for diverse applications and domains.</p>
</div>
</div>
</div>
</div>
</section><!-- End Services Section -->
<!-- ======= Clients Section ======= -->
<!-- <section id="clients">
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<h2>Clients</h2>
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<div class="swiper-slide"><img src="assets/img/clients/client-2.png" class="img-fluid" alt=""></div>
<div class="swiper-slide"><img src="assets/img/clients/client-3.png" class="img-fluid" alt=""></div>
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<!-- ======= Portfolio Section ======= -->
<section id="portfolio" class="portfolio">
<div class="container" data-aos="fade-up">
<div class="section-header">
<h2>Showcases</h2>
<!-- Timer拥有较强的泛化性，能够适应多种不同领域和场景的真实时序数据，在各种任务上拥有优异的处理效果，这里展现了Timer在不同数据上的真实表现 -->
<p>
Timer boasts an exceptional generalization capacity, adeptly accommodating a myriad of authentic time series datasets from diverse sectors and scenarios. It consistently delivers superior performance across a spectrum of tasks, showcasing its robust adaptability and efficacy. The following demonstration illustrates the tangible prowess of Timer when applied to a variety of data sets.
<br>The following visualization results are all from fine-tuning results under extremely low sample usage rates.
</p>
</div>
<div class="row" data-aos="fade-up" data-aos-delay="100">
<div class="col-lg-12 d-flex justify-content-center">
<ul id="portfolio-flters">
<li data-filter="*" class="filter-active">All</li>
<li data-filter=".filter-forecast">Forecast</li>
<li data-filter=".filter-imputation">Imputation</li>
<li data-filter=".filter-detection">Detection</li>
</ul>
</div>
</div>
<div class="row portfolio-container" data-aos="fade-up" data-aos-delay="200">
<div class="col-lg-4 col-md-6 portfolio-item filter-forecast">
<img src="assets/img/portfolio/forecast_ECL_0.2_0.pdf.png" class="img-fluid" alt="">
<div class="portfolio-info">
<h4>Forecast on ECL Dataset</h4>
<p>20% Samples Fine-tuned</p>
<a href="assets/img/portfolio/forecast_ECL_0.2_0.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
</div>
</div>
<div class="col-lg-4 col-md-6 portfolio-item filter-forecast">
<img src="assets/img/portfolio/forecast_Traffic_0.2_10_0.pdf.png" class="img-fluid" alt="">
<div class="portfolio-info">
<h4>Forecast on Traffic Dataset</h4>
<p>20% Samples Fine-tuned</p>
<a href="assets/img/portfolio/forecast_Traffic_0.2_10_0.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
</div>
</div>
<div class="col-lg-4 col-md-6 portfolio-item filter-forecast">
<img src="assets/img/portfolio/forecast_PEMS03_0.2_30_0.pdf.png" class="img-fluid" alt="">
<div class="portfolio-info">
<h4>Forecast on PEMS03 Dataset</h4>
<p>20% Samples Fine-tuned</p>
<a href="assets/img/portfolio/forecast_PEMS03_0.2_30_0.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
</div>
</div>
<div class="col-lg-4 col-md-6 portfolio-item filter-forecast">
<img src="assets/img/portfolio/forecast_ECL_0.05_10_0.pdf.png" class="img-fluid" alt="">
<div class="portfolio-info">
<h4>Forecast on ECL Dataset</h4>
<p>5% Samples Fine-tuned</p>
<a href="assets/img/portfolio/forecast_ECL_0.05_10_0.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
</div>
</div>
<div class="col-lg-4 col-md-6 portfolio-item filter-forecast">
<img src="assets/img/portfolio/forecast_Traffic_0.05_10_0.pdf.png" class="img-fluid" alt="">
<div class="portfolio-info">
<h4>Forecast on Traffic Dataset</h4>
<p>5% Samples Fine-tuned</p>
<a href="assets/img/portfolio/forecast_Traffic_0.05_10_0.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
</div>
</div>
<div class="col-lg-4 col-md-6 portfolio-item filter-forecast">
<img src="assets/img/portfolio/forecast_PEMS03_0.05_30_0.pdf.png" class="img-fluid" alt="">
<div class="portfolio-info">
<h4>Forecast on PEMS03 Dataset</h4>
<p>5% Samples Fine-tuned</p>
<a href="assets/img/portfolio/forecast_PEMS03_0.05_30_0.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
</div>
</div>
<div class="col-lg-4 col-md-6 portfolio-item filter-imputation">
<img src="assets/img/portfolio/imputation_ETTh2_0.2_400.pdf.png" class="img-fluid" alt="">
<div class="portfolio-info">
<h4>Imputation on ETTh2 Dataset</h4>
<p>20% Samples Fine-tuned</p>
<a href="assets/img/portfolio/imputation_ETTh2_0.2_400.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
</div>
</div>
<div class="col-lg-4 col-md-6 portfolio-item filter-imputation">
<img src="assets/img/portfolio/imputation_ECL_0.2_140.pdf.png" class="img-fluid" alt="">
<div class="portfolio-info">
<h4>Imputation on ECL Dataset</h4>
<p>20% Samples Fine-tuned</p>
<a href="assets/img/portfolio/imputation_ECL_0.2_140.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
</div>
</div>
<div class="col-lg-4 col-md-6 portfolio-item filter-imputation">
<img src="assets/img/portfolio/imputation_Traffic_0.2_180.pdf.png" class="img-fluid" alt="">
<div class="portfolio-info">
<h4>Imputation on Traffic Dataset</h4>
<p>20% Samples Fine-tuned</p>
<a href="assets/img/portfolio/imputation_Traffic_0.2_180.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
</div>
</div>
<div class="col-lg-4 col-md-6 portfolio-item filter-imputation">
<img src="assets/img/portfolio/imputation_ETTh2_0.05_400.pdf.png" class="img-fluid" alt="">
<div class="portfolio-info">
<h4>Imputation on ETTh2 Dataset</h4>
<p>5% Samples Fine-tuned</p>
<a href="assets/img/portfolio/imputation_ETTh2_0.05_400.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
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<h4>Imputation on ECL Dataset</h4>
<p>5% Samples Fine-tuned</p>
<a href="assets/img/portfolio/imputation_ECL_0.05_140.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
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<img src="assets/img/portfolio/imputation_Traffic_0.05_180.pdf.png" class="img-fluid" alt="">
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<h4>Imputation on Traffic Dataset</h4>
<p>5% Samples Fine-tuned</p>
<a href="assets/img/portfolio/imputation_Traffic_0.05_180.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
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<div class="portfolio-info">
<h4>Detection on ECG4 Dataset</h4>
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<h4>Detection on insectEPG5 Dataset</h4>
<a href="assets/img/portfolio/detection_069_UCR_Anomaly_DISTORTEDinsectEPG5_3200_8500_8501.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
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<h4>Detection on CHARISten Dataset</h4>
<a href="assets/img/portfolio/detection_206_UCR_Anomaly_CHARISten_25130_29080_29140.pdf.png" data-gallery="portfolioGallery" class="portfolio-lightbox preview-link"><i class="bx bx-plus"></i></a>
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