The paper introduces a fast algorithm for learning and forecasting nonlinear dynamics from noisy time series data.
problem Challenges in capturing nonlinear dynamics from noisy time series data.
method A projected nonlinear state-space model with kernel functions applied to projected lines.
result The model effectively learns and forecasts complex nonlinear dynamics with computational efficiency.
Machine Learning improves macroeconomic forecasting by capturing nonlinearities.
problem Improving macroeconomic forecasting accuracy.
method Study four features (nonlinearities, regularization, cross-validation, loss function) in data-rich and data-poor environments.
result Nonlinearity is the key to improving forecasting accuracy.
Generalizes memory and forecasting capacities for nonlinear recurrent networks with dependent inputs.
problem Understanding memory and forecasting capabilities in networks with dependent inputs.
method Formulated bounds for memory and forecasting capacities in terms of network size and input properties.
result Proved that memory capacity for linear recurrent networks with independent inputs is given by the rank of the controllability matrix.
Novel time series forecasting method using sliding window signatures.
problem Challenges in forecasting nonlinear and delayed time series data.
method Ridge regression with signature features calculated on sliding windows.
result Signature features effectively encode temporal and nonlinear dependencies, leading to accurate forecasts.
Paper proposes forecast-necessity testing for accurate causal interpretation in nonlinear time-series models.
problem Misinterpretation of causal scores from nonlinear models as regression coefficients.
method Systematic edge ablation and forecast comparison to evaluate causal necessity.
result Causal relationships with similar scores can differ in their necessity for accurate prediction.
Bayesian RNN model forecasts and quantifies uncertainty in spatio-temporal data.
problem Uncertainty quantification in nonlinear spatio-temporal systems.
method Developed a Bayesian RNN model to forecast and quantify uncertainty rigorously.
result The model maintains forecast accuracy while quantifying uncertainty formally.
New hybrid method combines ARIMA and ANN for better time series forecasting.
problem Improving forecasting accuracy of time series data.
method ARIMA-ANN hybrid method with empirical mode decomposition strategies.
result Our hybrid method outperforms traditional methods in forecasting accuracy.
DeepEDM forecasts time series by learning dynamics from embeddings.
problem Precise future prediction of complex nonlinear time series.
method Integrates nonlinear dynamical systems modeling with deep neural networks.
result DeepEDM outperforms state-of-the-art methods in forecasting accuracy.
Improved forecasting in big data with adaptive shrinkage neural networks.
problem Forecasting in high-dimensional, possibly nonlinear settings with high error rates.
method Adaptive shrinkage estimation of a deep neural network with skip-layer connections, incorporating L1 and L2 penalties.
result Robust predictions with improved reproducibility and enhanced forecast performance.
ESN enhances forecasting for nonlinear spatio-temporal data.
problem Forecasting nonlinear spatio-temporal processes efficiently.
method Enhanced ESN machine learning approach.
result Reasonable uncertainty quantification for long-lead forecasts.
Spectral methods predict long-term signals from linear and nonlinear systems.
problem Forecasting temporal signals from linear and nonlinear systems with arbitrary sampling.
method Introduces a spectral algorithm for linear signals and extends it to nonlinear systems using Koopman theory.
result The spectral methods achieve high accuracy in forecasting and uncertainty quantification.
The paper uses machine learning to forecast macroeconomic outcomes with high-dimensional data.
problem Forecasting the full conditional distribution of macroeconomic outcomes.
method Systematically integrating three key principles: high-dimensional data with regularization, rigorous out-of-sample validation, and incorporating nonlinearities.
result Regularization via shrinkage is essential to control model complexity, while nonlinearities yield limited improvements in predictive accuracy.
Machine learning improves economic forecasting during the pandemic.
problem Forecasting economic downturns during the COVID-19 pandemic.
method Use of machine learning methods to capture nonlinearity in macroeconomic data.
result Some nonlinear ML methods can extrapolate and improve forecasting accuracy.
We develop a new DTSM with nonlinearities using Gaussian Processes for better interest rate forecasting.
problem Linear DTSMs fail to capture nonlinear relationships between macroeconomic variables and interest rates.
method We propose a Gaussian Process-based sequential Monte Carlo estimation and forecasting scheme.
result Nonlinear models outperform linear ones in forecasting core inflation, leading to significant economic value gains.
Transformations of macroeconomic data affect machine learning forecasts, especially with regularization and nonlinearity.
problem The impact of data transformations on machine learning forecasts in macroeconomic contexts.
method Review and propose new data transformations, empirically evaluate their effects, and compare traditional and moving average rotations.
result Traditional factors should almost always be included as predictors, and moving average rotations can provide important gains.
VANAR outperforms VAR in forecasting and causality detection.
problem Capturing nonlinearity in dynamical systems for accurate predictions and causal relationships.
method Introduces VANAR, a neural network for nonlinear autoregression.
result VANAR significantly outperforms VAR in forecast and causality tests.
Bayesian model uses simple functions to forecast macroeconomic data.
problem Forecasting large datasets in macroeconomics with complex nonlinear relationships.
method Sum of simple two-component location mixtures, logistic function threshold, conjugate priors.
result Accurate point and density forecasts in US macroeconomic aggregates.
Neural ARFIMA model improves exchange rate forecasting for BRIC economies.
problem Forecasting exchange rates for emerging markets with long-term memory and nonlinear dynamics.
method Integrates ARFIMA for long-memory with neural networks for nonlinear approximation.
result NARFIMA model outperforms benchmarks in BRIC exchange rate forecasting.
This paper reviews forecast combinations over 50 years, highlighting their evolution and utility.
problem Improving forecast accuracy through combining multiple forecasts.
method Evolution of forecast combination methods, from simple to sophisticated.
result Forecast combinations have become a mainstream approach in forecasting.
Proposes neural dynamic mode decomposition for end-to-end modeling of nonlinear dynamics.
problem Understanding and modeling nonlinear dynamical systems.
method Trains neural networks to minimize forecast error based on spectral decomposition in the lifted space.
result Demonstrates effectiveness in eigenvalue estimation and forecast performance.
In order to obtain a reasonable and reliable forecast method for crude oil price volatility, this paper evaluates the forecast performance of single-regime GARCH models (including the standard linear GARCH model and the nonlinear GJR-GARCH and EGARCH models) and the two-regime Markov Regime Switching GARCH (MRS-GARCH) …
Stanza models complex time series with balance between traditional and deep learning approaches.
problem Capturing long-term structure in non-stationary time series.
method Nonlinear, non-stationary state space model.
result Achieves forecasting accuracy competitive with deep LSTMs, especially for multi-step ahead forecasting.
In this paper we introduce a simple continuous-time asset pricing framework, based on general multi-dimensional diffusion processes, that combines semi-analytic pricing with a nonlinear specification for the market price of risk. Our framework guarantees existence of weak solutions of the nonlinear SDEs under the physi…
Algorithm learns neural architecture for financial time-series forecasting.
problem Challenges in forecasting financial time-series data due to nonstationary property and nonlinear dependencies.
method Adaptive learning of a heterogeneous neural architecture using a modified objective function to handle imbalanced data.
result The proposed algorithm outperforms tensor-based methods in financial time-series forecasting.
The study improves load forecasting for electricity consumers using advanced machine learning models.
problem Improving short-term load forecasting for effective scheduling and decision-making.
method Proposes and evaluates statistical nonlinear models, including LSTM and GRU, for 15-min frequency electricity load forecasting.
result Advanced models outperform other models in out-of-sample forecasting accuracy, as shown by the Diebold-Mariano test.
Study predicts bond yields using machine learning and ultimate forward rates.
problem Forecasting bond yields using ultimate forward rates.
method Applied de Kort-Vellekooptype methodology for UFR estimation, used linear and nonlinear machine learning techniques.
result Nonlinear machine learning models outperform linear models in bond yield forecasting.
The leverage effect-- the correlation between an asset's return and its volatility-- has played a key role in forecasting and understanding volatility and risk. While it is a long standing consensus that leverage effects exist and improve forecasts, empirical evidence paradoxically do not show that most individual stoc…
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…
ProFnet models HDFTS with neural networks, offering scalable probabilistic forecasts.
problem Modeling high-dimensional functional time series with nonlinear trends and high spatial dimensions.
method Integrates feedforward and deep neural networks with probabilistic modeling.
result Superior performance in forecasting Japan's mortality rates.
Survey of machine learning methods for time series forecasting.
problem Improving accuracy of time series predictions.
method Linear and nonlinear machine learning models, including neural networks and ensemble methods.
result Demonstrates superior predictive ability of certain machine learning models.
Bayesian method forecasts market risks using expectiles and expected shortfall.
problem Forecasting market risks with high precision.
method Bayesian Markov Chain Monte Carlo method with nonlinear threshold specification.
result Empirical support for the proposed models in market indices forecasting.
Graph neural networks improve volatility forecasting by capturing spillover effects.
problem Forecasting multivariate realized volatility with spillover effects.
method Customized graph neural networks incorporating spillover effects from multi-hop neighbors.
result Modeling nonlinear spillover effects enhances forecasting accuracy, especially for short-term horizons.
Bayesian neural networks improve macroeconomic forecasting and model nonlinearities.
problem Handling small T, big K macroeconomic datasets with temporal dependence.
method Developed Bayesian neural networks with mixture activation functions, shrinkage priors, and stochastic volatility.
result BNNs produce precise density forecasts, often better than other methods.
A new method models financial returns by separating sign and magnitude, improving forecasting accuracy.
problem Capturing nonlinear predictability in financial return dynamics.
method Decomposes returns into sign and magnitude components, using a joint distribution model.
result Significantly outperforms traditional linear models in forecasting U.S. stock market returns.
The paper investigates non-linear and heavy-tailed predictability in transition-energy financial markets.
problem Incomplete representation of dependence structure in Gaussian-linear forecasting frameworks.
method Develops a hybrid forecasting framework combining Student-t Vector Autoregressions with nonlinear recurrent residual learning architectures.
result The proposed framework consistently improves predictive accuracy relative to conventional models, especially during macro-financial stress.
LGB+ improves macroeconomic forecasting by combining linear and tree models.
problem Efficiency in small samples for forecasting with mixed linear and nonlinear dynamics.
method LGB+ is a boosting procedure that evaluates both tree and linear candidates at each step, advancing only the winner. It decomposes forecasts into linear and nonlinear contributions.
result LGB+ delivers strong gains for targets with pronounced autoregressive dynamics or mixed signals.
Kernel Three-Pass Regression Filter improves forecasting efficiency for nonlinear dependencies.
problem Forecasting with high-dimensional predictors and latent factors.
method Developed a new estimator, Kernel Three-Pass Regression Filter (K3PRF), to address nonlinear dependencies.
result Empirically shows significant improvement in long-term forecasting performance.
Panda predicts chaotic systems without retraining, showing emergent properties.
problem Predicting chaotic systems with small errors.
method Trained on a synthetic dataset of chaotic dynamical systems using evolutionary algorithms.
result Panda predicts unseen chaotic systems with zero-shot learning.
Enhanced GARCH model uses autoencoder for volatility forecasting.
problem Selecting optimal realised volatility estimator for forecasting.
method Proposes an autoencoder-enhanced Realised GARCH model combining multiple realised measures.
result The model outperforms traditional linear methods in one-step-ahead rolling volatility forecasting.
New method combines long-memory reservoirs for accurate dengue forecasting from short data.
problem Accurate dengue forecasting from short, noisy, non-stationary, and nonlinear data.
method Fractional ESN and Wavelet ESN frameworks integrating long-term memory.
result fESN and wESN outperform baselines in multiple dengue datasets and forecasting horizons.
Bayesian framework forecasts financial tail risks using realized volatility and nonlinear thresholds.
problem Forecasting financial tail risks using realized volatility and nonlinear thresholds.
method Bayesian Markov Chain Monte Carlo method for model estimation; nonlinear threshold regression specification.
result The proposed framework produces competitive tail risk forecasts compared to GARCH and Realized-GARCH models.
TK-GCN forecasts spatiotemporal dynamics using Koopman-enhanced graph convolutional networks.
problem Forecasting complex spatiotemporal dynamics over irregular domains.
method Two-stage framework: Koopman-enhanced Graph Convolutional Network (K-GCN) for spatial encoding and Transformer for temporal modeling.
result TK-GCN outperforms state-of-the-art methods in spatiotemporal cardiac dynamics forecasting.
GraphSVR forecasts urban air pollution robustly across stations and seasons.
problem Nonlinear, nonstationary, spatiotemporally dependent urban air pollution forecasting challenges.
method Combines graph convolutional learning and support vector regression.
result GraphSVR improves predictive accuracy and maintains stable performance across seasons and outlier-prone episodes.
AdaKoop efficiently models nonlinear dynamics from nonstationary data streams.
problem Capturing nonlinear dynamics in nonstationary data streams with computational efficiency.
method Koopman operator theory and probabilistic framework for streaming data.
result AdaKoop outperforms state-of-the-art methods in real-time forecasting accuracy and efficiency.
DAISI improves data assimilation for complex systems with noisy observations.
problem Limited accuracy of classical DA methods in complex, nonlinear systems.
method Generative models with inverse sampling for flexible probabilistic inference.
result DAISI achieves accurate filtering results in challenging nonlinear systems.
Deep ESN models forecast spatio-temporal data with uncertainty quantification.
problem Complex nonlinear dynamics in spatio-temporal systems are hard to model.
method Deep ensemble ESN models using bootstrap and hierarchical Bayesian frameworks.
result Models produce forecasts and uncertainty measures for spatio-temporal data.
Two-stage method improves solar forecasting accuracy.
problem Inaccurate solar forecasting due to nonstationary data.
method Two-stage approach: linear and nonlinear forecasting, data processing.
result Improved accuracy of solar forecasting results.
The study examines the discrepancies between binary forecasts and real-world outcomes, revealing their often misleading nature.
problem The confusion between binary forecasts and real-world payoffs in decision-making and prediction.
method Comparative analysis of binary forecasts, bets, and real-world continuous payoffs under different tail conditions.
result Binary forecasting abilities do not translate to better real-world performance, and vice versa, especially under nonlinearities.