Results 11 to 20 of about 2,067,646 (378)
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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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
semanticscholar +1 more source
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
semanticscholar +1 more source
Forecast with forecasts: Diversity matters [PDF]
European Journal of Operational Research, 2022 Forecast combinations have been widely applied in the last few decades to improve forecasting. Estimating optimal weights that can outperform simple averages is not always an easy task. In recent years, the idea of using time series features for forecast combination has flourished.
Yanfei Kang +3 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
semanticscholar +1 more source
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting [PDF]
NeurIPS Datasets and Benchmarks, 2023 We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ...
Benjamin Wilson +11 more
semanticscholar +1 more source
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
semanticscholar +1 more source
Forecasting FOMC Forecasts [PDF]
SSRN Electronic Journal, 2018 (Hendry 1980, p. 403) The three golden rules of econometrics are “test, test, and test”. The current paper applies that approach to model the forecasts of the Federal Open Market Committee over 1992–2019 and to forecast those forecasts themselves.
S. Yanki Kalfa, Jaime Marquez
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Forecasting Professional Forecasters [PDF]
Journal of Business & Economic Statistics, 2006 Survey of forecasters, containing respondents' predictions of future values of growth, inflation and other key macroeconomic variables, receive a lot of attention in the financial press, from investors, and from policy makers. They are apparently widely perceived to provide useful information about agents' expectations.
Eric Ghysels, Jonathan H. Wright
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Using Forecasts of Forecasters to Forecast [PDF]
SSRN Electronic Journal, 2006 Quantification techniques are popular methods in empirical research to aggregate the qualitative predictions at the micro-level into a single figure. In this paper, we analyze the forecasting performance of various methods that are based on the qualitative predictions of financial experts for major financial variables and macroeconomic aggregates ...
Nolte, Ingmar, Pohlmeier, Winfried
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