Deep LPPLS: Forecasting of temporal critical points in natural, engineering and financial systems

Joshua Nielsen, Didier Sornette and Maziar Raissi
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Joshua Nielsen: University of Colorado, Boulder
Didier Sornette: Risks-X, Southern University of Science and Technology (SUSTech); Swiss Finance Institute; ETH Zürich - Department of Management, Technology, and Economics (D-MTEC); Tokyo Institute of Technology
Maziar Raissi: University of California, Riverside

No 24-33, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: The Log-Periodic Power Law Singularity (LPPLS) model offers a general framework for capturing dynamics and predicting transition points in diverse natural and social systems. In this work, we present two calibration techniques for the LPPLS model using deep learning. First, we introduce the Mono-LPPLS-NN (M-LNN) model; for any given empirical time series, a unique M-LNN model is trained and shown to outperform state-of-the-art techniques in estimating the nonlinear parameters (tc; m; !) of the LPPLS model as evidenced by the comprehensive distribution of parameter errors. Second, we extend the M-LNN model to a more general model architecture, the Poly-LPPLS-NN (P-LNN), which is able to quickly estimate the nonlinear parameters of the LPPLS model for any given time-series of a fixed length, including previously unseen time-series during training. The Poly class of models train on many synthetic LPPLS time-series augmented with various noise structures in a supervised manner. Given enough training examples, the P-LNN models also outperform state-of-the-art techniques for estimating the parameters of the LPPLS model as evidenced by the comprehensive distribution of parameter errors. Additionally, this class of models is shown to substantially reduce the time to obtain parameter estimates. Finally, we present applications to the diagnostic and prediction of two financial bubble peaks (followed by their crash) and of a famous rockslide. These contributions provide a bridge between deep learning and the study of the prediction of transition times in complex time series.

Keywords: log-periodicity; finite-time singularity; prediction; change of regime; financial bubbles; landslides; deep learning (search for similar items in EconPapers)
JEL-codes: C00 C13 C69 G01 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2024-05
New Economics Papers: this item is included in nep-big and nep-ecm
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2433

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