tsGT: Stochastic Time Series Modeling With Transformer

• URL: http://arxiv.org/abs/2403.05713v3
• Date: Wed, 3 Apr 2024 17:17:21 GMT
• Title: tsGT: Stochastic Time Series Modeling With Transformer
• Authors: Łukasz Kuciński, Witold Drzewakowski, Mateusz Olko, Piotr Kozakowski, Łukasz Maziarka, Marta Emilia Nowakowska, Łukasz Kaiser, Piotr Miłoś,
• Abstract summary: We introduce tsGT, a time series model built on a general-purpose transformer architecture. We show that tsGT outperforms the state-of-the-art models on MAD and RMSE, and surpasses its peers on QL and CRPS, on four commonly used datasets.
• Score: 0.12905935507312413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series methods are of fundamental importance in virtually any field of science that deals with temporally structured data. Recently, there has been a surge of deterministic transformer models with time series-specific architectural biases. In this paper, we go in a different direction by introducing tsGT, a stochastic time series model built on a general-purpose transformer architecture. We focus on using a well-known and theoretically justified rolling window backtesting and evaluation protocol. We show that tsGT outperforms the state-of-the-art models on MAD and RMSE, and surpasses its stochastic peers on QL and CRPS, on four commonly used datasets. We complement these results with a detailed analysis of tsGT's ability to model the data distribution and predict marginal quantile values.

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