Pretraining large models on time series data faces several challenges. The lack of a comprehensive public time series repository, the complexity of diverse time series characteristics, and the early stages of experimental benchmarking for model evaluation, especially under resource-constrained and minimally monitored scenarios. . Despite these hurdles, time series analysis remains important across applications such as weather forecasting, heart rate anomaly detection, and identifying anomalies in software deployment. Leveraging pre-trained linguistic, visual, and video models is promising, although they must be adapted to the time-series data specifications for optimal performance.
Applying transformers to time series analysis poses challenges because the self-attention mechanism grows quadratically with input token size. Treating subsequences of a time series as tokens improves the efficiency and effectiveness of predictions. ORCA leverages cross-modal transfer learning from language models to extend pre-trained models to diverse modalities through fine-tuning and refinement. Recent research has utilized this approach to reprogram the language's pre-trained transformers for time series analysis, but resource-intensive models require large amounts of memory and computational resources for optimal performance. I need it.
Researchers from Carnegie Mellon University and the University of Pennsylvania have announced MOMENT, an open-source family of foundational models for general-purpose time series analysis. Leverage the Time series Pile, a diverse collection of public time series, to address time series-specific challenges and enable pre-training on large multi-datasets. These high-capacity transformer models are pre-trained with masked time series prediction tasks on a wide range of data from different domains, making them versatile and robust in tackling diverse time series analysis tasks. We provide.
To address the lack of comprehensive time series datasets, MOMENT begins by combining datasets from different repositories to assemble a diverse collection of public time series data called the Time Series Pile. These datasets include long-term prediction, short-term prediction, classification, and anomaly detection tasks. MOMENT's architecture includes a transformer encoder and a lightweight reconstruction head pretrained on a masked time series prediction task. The pre-training setup includes variations of his MOMENT for different sized encoders, trained with the Adam optimizer and gradient checkpoints for memory optimization. MOMENT is designed to fine-tune downstream tasks such as prediction, classification, anomaly detection, and imputation using end-to-end or linear probes, depending on the task requirements.
This study compares MOMENT with state-of-the-art deep learning and statistical machine learning models across a variety of tasks, in contrast to TimesNet, which primarily focuses on transformer-based approaches. These comparisons are essential to assess the practical applicability of the proposed method. Interestingly, statistical and non-transformer-based techniques such as ARIMA for short-term predictions, N-BEATS for long-term predictions, and k-nearest neighbors for anomaly detection outperform many deep learning and transformer-based models. It shows excellent performance. .
In summary, this study introduces MOMENT, the first open source time series fundamental model family developed through comprehensive stages of data editing, model pre-training, and systematically addressing time series-specific challenges. To do. By leveraging Time Series Piles and innovative strategies, MOMENT provides high performance in pre-training transformer models of various sizes. This study also designs experimental benchmarks to evaluate time-series foundational models across multiple practical tasks, with a special focus on scenarios with limited computational resources and monitoring. MOMENT shows effectiveness across a variety of tasks and, thanks to its pre-training, performs especially well in anomaly detection and classification. This study also highlights the feasibility of smaller, shallower statistical deep learning methods across many tasks. Ultimately, this research aims to advance open science by releasing time series piles along with code, model weights, and training logs to foster collaboration and further advance time series analysis. Masu.
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Asjad is an intern consultant at Marktechpost. He is pursuing a degree in mechanical engineering from the Indian Institute of Technology, Kharagpur. Asjad is a machine learning and deep learning enthusiast and is constantly researching applications of machine learning in healthcare.
