TDA detects financial bubbles through early warning signals.
problem Detecting financial bubbles early.
method Using Log-Periodic Power Law Singularity (LPPLS) model to fit financial time series data.
result TDA generates early warning signals when LPPLS model fits the data.
This study uses high-frequency data to identify early warning signals for bank crises.
problem Identifying early warning signals for impending bank crises.
method Constructing multiple recurrence networks (MRNs) based on high-frequency stock returns to monitor nonlinear dynamics.
result Key indicators of MRNs, particularly average mutual information, provide valuable insights into periods of extreme volatility.
Study proposes a new early-warning framework for high-dimensional complex systems.
problem Predicting critical transitions in complex systems like epileptic seizures.
method Integrates manifold learning with stochastic dynamical system modeling, using Schrödinger bridge theory.
result Demonstrates higher sensitivity and robustness in epilepsy prediction.
Study uses DNM theory to detect early warning signals of market instability.
problem Detecting early warning signals of financial market instability.
method Applying Dynamical Network Marker (DNM) theory to trading data from the Tokyo Stock Exchange.
result Early warning signals of large price movements can be detected on a daily time scale.
Novel approach detects early warning indicators in complex systems.
problem Detecting abrupt transitions in complex systems.
method Directed anisotropic diffusion map and latent stochastic dynamical systems.
result Early warning indicators can detect tipping points in state transitions.
Economic integration, globalization and financial crises represent examples of processes whose understanding requires the analysis of the underlying network structure. Of particular interest is establishing whether a real economic network is in a state of (quasi)stationary equilibrium, i.e. characterized by smooth stru…
Machine learning detects regime shifts in online game-experiments with high accuracy.
problem Detecting regime shifts in online social systems.
method Gradient-boosted decision trees with memory-retaining features.
result Significantly outperforms standard early warning indicators.
Crypto crashes show no consistent early warning signal, suggesting they are abrupt shocks rather than critical transitions.
problem Identifying early warning signals for crypto crashes.
method Analysis of seven major BTC liquidation cascades using minute-level price and leverage/order-flow data.
result No variable is event-invariant, and the critical-slowing-down signature is present in only five out of seven events.
The paper analyzes early signs of the 2007-2008 financial crisis in world trade.
problem Detecting early signs of the 2007-2008 financial crisis in world trade.
method Employed network theory to analyze the World Trade Web (WTW) before and after 2007.
result The WTW shows early structural changes in 2003, indicating the loss of correlations between countries and products.
This work uses transfer entropy to reconstruct influential network between global stock markets.
problem Noise in network structure due to mismatch of scale between single market and global markets.
method Reconstructing influential network between ten typical stock markets using transfer entropy.
result Before financial crises, connection strength between markets reaches a maximum, acting as an early warning signal.
The financial crisis clearly illustrated the importance of characterizing the level of 'systemic' risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly inter…
Method recognizes chaotic regime in crypto markets.
problem Detecting critical transitions in crypto markets.
method Topological data analysis + k-means clustering.
result Early warning signals for crypto market crashes.
The principal aim of this work is the evidence on empirical way that catastrophic bifurcation breakdowns or transitions, proceeded by flickering phenomenon, are present on notoriously significant and unpredictable financial markets. Overall, in this work we developed various metrics associated with catastrophic bifurca…
Novel SCUSUM detects weak spatial signals more efficiently.
problem Detecting weak clustered signals in spatial data.
method Spatial CUSUM (SCUSUM) using CUSUM procedure and false discovery rate control.
result SCUSUM achieves high classification accuracy for weak spatial signals.
We take prior-to-crash market prices (NASDAQ, Dow Jones Industrial Average) as a signal, a function of time, we project these discrete values onto a vertical axis, thus obtaining a Cantordust. We study said cantordust with the tools of multifractal analysis, obtaining spectra by definition and by lagrangian coordinates…
Human stablecoin transactions predict political risk in cryptocurrency markets.
problem Predicting political risk in cryptocurrency markets.
method Structural break analysis and surrogate-based robustness tests.
result Human-driven stablecoin transactions shift significantly before major political events.
This study analyzes refugee influx patterns in Greece using model-theoretic techniques.
problem Lack of early warning systems for refugee arrivals in Greece.
method Statistical and signal-based techniques including regression, spectral analysis, and matrix factorization.
result Behavioral patterns of smuggling networks match store-and-forward networks in digital communications.
Clusters cryptocurrency market states via cross correlation analysis.
problem Analyse cryptocurrency market dynamics.
method Cross correlation structure analysis over 5 years.
result Cryptocurrency market clusters into 4 states.
Study improves early warning models for currency and stock market crises.
problem Predicting currency and stock market crises.
method Synthetic review and comparison of early warning models, focusing on crisis identifications and predictive models.
result SWARCH model with elastic thresholding methodology most accurately classifies crisis observations.
SRR detects early signs of financial crises using multi-layer graphs.
problem Predicting systemic financial transitions from evolving market interactions.
method Systemic Risk Radar (SRR) models financial markets as multi-layer graphs.
result Graph-derived features provide useful early-warning signals compared to feature-based models.
New model predicts financial market abnormalities using stock index uncertainties.
problem Forecasting abnormal financial fluctuations in the market.
method Quantitative analysis of mean and volatility uncertainties, constructing early warning indicators.
result Established a new abnormal fluctuations warning model.
TDA detects topological patterns in financial crashes.
problem Detecting early warning signals of financial crashes.
method Topological Data Analysis (TDA) with persistence homology.
result Persistence landscapes exhibit strong growth before financial meltdowns.
New method detects bearing faults using multivariate statistical process control.
problem Early detection of bearing faults in rotating machinery.
method Multivariate statistical process control charts applied to Fourier transform features of fixed-time batches.
result Effectiveness in detecting bearing faults across different conditions.
We introduce an event based framework of directional changes and overshoots to map continuous financial data into the so-called Intrinsic Network - a state based discretisation of intrinsically dissected time series. Defining a method for state contraction of Intrinsic Network, we show that it has a consistent hierarch…
Study builds an EWS for early detection of stock market turbulence.
problem Early detection of stock market turbulence.
method SWARCH filtering probabilities, two-peak method, LSTM network.
result 96.6% test-set accuracy and 2.4 days of forewarned period.
The paper develops a method to predict the latent deterioration phase in limit order books before stress is observed.
problem Limit order books can transition rapidly from stable to stressed conditions, making it difficult to detect the latent deterioration phase.
method The paper formalizes a three-regime causal data-generating process and proposes a trigger-based detector combining MAX aggregation of complementary signal channels, a rising-edge condition, and adaptive thresholding.
result The proposed method achieves mean lead-time of +18.6 timesteps with perfect precision and moderate coverage, outperforming classical change-point and microstructure baselines.
This paper presents first steps toward robust models for crisis prediction. We conduct a horse race of conventional statistical methods and more recent machine learning methods as early-warning models. As individual models are in the literature most often built in isolation of other methods, the exercise is of high rel…
Trading strategy uses Hoeffding's Inequality to predict financial regime change.
problem Predicting financial regime change for trading strategies.
method Applies Hoeffding's Inequality to trading performance data.
result Early warning of financial regime change can be detected.
Early warning signs of Greece's economic crisis were present in GDP growth rate instability.
problem Identifying early warning signs of economic crises in other countries.
method Analysis of GDP growth rate stability and long-term trends.
result GDP growth rate instability predicted the economic collapse in Greece.
This review examines EWSs in finance to prevent economic crises.
problem Detecting financial distress early to prevent crises.
method Comprehensive bibliometric review of 616 articles from 1976 to 2023.
result Recent research emphasizes machine learning techniques and alternative data sources.
The paper models market crashes as phase transitions, finding dynamic transitions offer better predictions.
problem Understanding and predicting extreme financial events like market crashes.
method Employing phase transition theory, focusing on endogenous crashes, and comparing DPT, CPT, and SPT.
result Dynamic phase transitions provide more accurate predictions of market crashes compared to critical and stochastic models.
Given a nonlinear model, a probabilistic forecast may be obtained by Monte Carlo simulations. At a given forecast horizon, Monte Carlo simulations yield sets of discrete forecasts, which can be converted to density forecasts. The resulting density forecasts will inevitably be downgraded by model mis-specification. In o…
This work proposes an augmented variant of DebtRank with uncertainty intervals as a method to investigate and assess systemic risk in financial networks, in a context of incomplete data. The algorithm is tested against a default contagion algorithm on three ensembles of networks with increasing density, estimated from …
AI system predicts acute critical illness from EHRs with explainability.
problem Lack of clinical interpretability in AI predictions for acute critical illness.
method Developed an explainable AI early warning score (xAI-EWS) system.
result System provides clinicians with insights into EHR data explaining predictions.
Team aims to predict particulate matter levels on ISS using Bi-GRU.
problem Early warning system for particulate matter on ISS.
method Bi-GRU algorithm analyzing past 90 minutes of data.
result Bi-GRU predicts particulate matter levels up to 1 minute in advance.
Paper proposes a new hybrid model for forecasting house prices.
problem Forecasting sudden house price drops to prevent financial crises.
method Combines EEMD signal processing with SVR machine learning.
result Proposed model outperforms other models with half the error.
Study forecasts food security trends using real-time data.
problem Food insecurity prediction for sub-national regions.
method Quantitative methodology combining various machine learning models.
result Reservoir Computing model performs best in food security prediction.
Financial markets modeled like brain networks using dMNC.
problem Understanding latent dynamics in financial markets.
method Biologically inspired framework using dMNC.
result Structural persistence, regime shifts, and early warning signals identified.
CNMs detect tipping points in complex systems using causal network markers.
problem Identifying tipping points ahead of critical transitions in complex systems.
method Introducing CNMs that incorporate causality indicators to detect tipping points.
result CNMs show higher predictive power and accuracy than traditional DNB indicators.
HyPV-LEAD detects cryptocurrency anomalies proactively, improving financial security.
problem Cryptocurrency anomalies like mixing, fraud, and pump-and-dump operations are hard to detect due to class imbalance and temporal volatility.
method HyPV-LEAD integrates lead time into anomaly detection through window-horizon modeling, Peak-Valley sampling, and hyperbolic embedding.
result HyPV-LEAD achieves a PR-AUC of 0.9624 on Bitcoin transaction data, significantly outperforming state-of-the-art methods.
Project forecasts liquidity withdrawal using machine learning models.
problem Predicting liquidity withdrawal at individual stock levels.
method Tested a framework using machine learning models (AR, HAR, XGBoost) on Nasdaq MBO data.
result Introduced the Liquidity Withdrawal Index (LWI) for measuring liquidity removal.
Study predicts crypto-currency price collapses using standard deviation.
problem Detecting price collapses in crypto-currencies.
method Phenomenological model and analysis of standard deviation.
result Standard deviation can predict crypto-currency price collapses.
Machine learning predicts circulatory failure in ICU patients.
problem Limited ability of clinicians to recognize early signs of patient deterioration.
method Developed an early warning system using machine learning on ICU data.
result Predicts 90.0% of circulatory failure events with 81.8% identified more than two hours in advance.
Paper detects social media influencers affecting financial markets.
problem Impact of social media influencers on financial markets.
method Developed an early warning system for detecting suspicious social network activity.
result Discrepancy in meme and non-meme stocks' reactions to social networks.
Study uses satellite data to predict tailings dam collapse risk.
problem Detecting early signs of tailings dam instability.
method Spectral analysis of satellite InSAR displacement time series data.
result Algorithm detects risk milestones up to 5 months before dam collapse.
A new method identifies critical transitions in high-dimensional data.
problem Challenges in identifying critical transitions in high-dimensional time-series data.
method Spatial-temporal Principal Component Analysis (stPCA)
result Identifies tipping points before critical transitions reliably.
Machine learning improves early detection of patient deterioration in Brazilian hospitals.
problem Challenges in recognizing clinical deterioration in hospital settings.
method Application of machine learning to analyze EHR data from multiple hospitals.
result Machine learning models outperformed traditional protocols by 25 percentage points in AUC.
QNA uses quantum-inspired density operators to diagnose market dependence and structural risk.
problem Lack of unified operator representation for market dependence and structural risk diagnostics.
method Quantum Network of Assets (QNA) framework using density operators.
result QNA entropy remains strongly related to covariance spectral entropy but becomes distinct with multi-feature rolling trajectories.