Results 11 to 20 of about 28,610,872 (373)
Oceanography, 2013 In this issue of Oceanography, Holliday and Cunningham (2013) extol the significance of long-term data sets in understanding the marine environment and, in particular, climate change. The 1950s were the years of exploration, dividing the ocean up into bite-sized chunks to explore as part of the International Geophysical Year(s). The 1960s and '70s were
Simon Boxall
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Distribution of the Estimators for Autoregressive Time Series with a Unit Root [PDF]
, 1979 Let n observations Y 1, Y 2, ···, Y n be generated by the model Y t = pY t−1 + e t , where Y 0 is a fixed constant and {e t } t-1 n is a sequence of independent normal random variables with mean 0 and variance σ2.
David A. Dickey, Wayne A. Fuller
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Are Transformers Effective for Time Series Forecasting? [PDF]
AAAI Conference on Artificial Intelligence, 2022 Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work.
Ailing Zeng +3 more
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A Time Series is Worth 64 Words: Long-term Forecasting with Transformers [PDF]
International Conference on Learning Representations, 2022 We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. It is based on two key components: (i) segmentation of time series into subseries-level patches which are ...
Yuqi Nie +3 more
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iTransformer: Inverted Transformers Are Effective for Time Series Forecasting [PDF]
International Conference on Learning Representations, 2023 The recent boom of linear forecasting models questions the ongoing passion for architectural modifications of Transformer-based forecasters. These forecasters leverage Transformers to model the global dependencies over temporal tokens of time series ...
Yong Liu +6 more
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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting [PDF]
AAAI Conference on Artificial Intelligence, 2020 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 ...
Haoyi Zhou +6 more
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Time-LLM: Time Series Forecasting by Reprogramming Large Language Models [PDF]
International Conference on Learning Representations, 2023 Time series forecasting holds significant importance in many real-world dynamic systems and has been extensively studied. Unlike natural language process (NLP) and computer vision (CV), where a single large model can tackle multiple tasks, models for ...
Ming Jin +10 more
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Large Language Models Are Zero-Shot Time Series Forecasters [PDF]
Neural Information Processing Systems, 2023 By encoding time series as a string of numerical digits, we can frame time series forecasting as next-token prediction in text. Developing this approach, we find that large language models (LLMs) such as GPT-3 and LLaMA-2 can surprisingly zero-shot ...
Nate Gruver +3 more
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TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis [PDF]
International Conference on Learning Representations, 2022 Time series analysis is of immense importance in extensive applications, such as weather forecasting, anomaly detection, and action recognition. This paper focuses on temporal variation modeling, which is the common key problem of extensive analysis ...
Haixu Wu +5 more
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Graph Neural Network-Based Anomaly Detection in Multivariate Time Series [PDF]
AAAI Conference on Artificial Intelligence, 2021 Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains
Ailin Deng, Bryan Hooi
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