Transformer-based models overfit financial time series data, leading to increased prediction variance.
problem Forecast collapse of transformer-based models under squared loss in financial time series.
method Theoretical analysis and numerical experiments on high-frequency EUR/USD exchange rate data.
result Increased model expressivity in Transformer-based models leads to spurious fluctuations without reducing bias, resulting in higher prediction variance.
The paper proposes a method to improve forecast combination accuracy using portfolio theory.
problem Improving forecast accuracy by combining multiple forecasts.
method Generates forecast combinations using a portfolio analogy, allowing negative weights for hedging.
result Demonstrates improved performance in weighted random forest forecasts.
BSG learns dynamic network spillovers and uncertainty quantification.
problem Identifying indirect spillovers and systemic risk in dynamic networks.
method Bayesian Spillover Graphs using FEVD and Bayesian time series models.
result Significant performance gains over baselines in identifying source and sink nodes.
Study improves forecast accuracy of daily volatility to enhance portfolio performance.
problem Improving predictability of realized variance from market views.
method High-dimensional machine learning models and low-dimensional factor models used to forecast firm-level volatility.
result Marginal improvements in forecast error lead to significant gains in portfolio performance.
Study optimizes stock portfolios using network analysis and forecasting.
problem Optimizing stock portfolios with network analysis and forecasting.
method Constructs dependency networks using VAR and FEVD, applies MST algorithm, and incorporates ARIMA and NNAR forecasts.
result MST-based strategies outperform buy-and-hold benchmarks, achieving higher returns.
We investigate the forecasting ability of the most commonly used benchmarks in financial economics. We approach the usual caveats of probabilistic forecasts studies -small samples, limited models and non-holistic validations- by performing a comprehensive comparison of 15 predictive schemes during a time period of over…
Model predicts stock price changes and forecasts using tokenized data.
problem Challenges in stock price forecasting and prediction due to dynamic data and statistical differences.
method Introduces PCIE model with tokenization to handle both forecasting and prediction.
result PCIE model outperforms state-of-the-art models in forecast and prediction tasks.
Model forgets examples; this research predicts which ones to replay.
problem Language models forget examples during updates, leading to errors.
method Train forecasting models to predict which examples will be forgotten.
result Forecasting models can reduce forgetting of upstream pretraining examples.
Variant of mSSA improves time series prediction error.
problem Improve prediction error in multivariate time series.
method Introduce spatio-temporal factor model, establish prediction error scaling.
result Prediction error scales as 1 / √(min(N, T)T).
Enhanced TSFMs improve time series forecasting accuracy and reliability.
problem Variance, bias, and uncertainty in TSFMs' predictions on real data.
method Statistical and ensemble techniques including bagging, stacking, residual modeling, and prediction intervals.
result Hybrid models consistently outperform standalone TSFMs across multiple horizons.
Proposes using elastic demand to improve forecasting accuracy.
problem Mismatch between planned supply and actual demand due to demand variance.
method Reallocate historical elastic demand to reduce forecasting variance.
result Improves forecasting and supply planning effectiveness.
Improved LSTM models predict wind power more accurately with weather data.
problem Poor performance of generic LSTM models on wind power data.
method Contextualized LSTM models using weather forecast data and modifications to address model shortcomings.
result Increased accuracy and reduced naive character in predictions.
New method forecasts time series with changing variances.
problem Real-world processes with changing variances cannot be captured by classical models.
method State-space model with Markov switching variances, using online learning and expert aggregation.
result Proposed method outperforms traditional expert aggregation and is robust to misspecification.
In this paper we forecast daily returns of crypto-currencies using a wide variety of different econometric models. To capture salient features commonly observed in financial time series like rapid changes in the conditional variance, non-normality of the measurement errors and sharply increasing trends, we develop a ti…
Study finds significant but limited connections between cryptocurrencies and mainstream asset classes.
problem Investigating the relationship between cryptocurrencies and traditional asset classes.
method Granger-causality tests and forecast error variance decompositions to estimate connectedness.
result Less than 2.2% of cryptocurrency uncertainty is from non-crypto assets, and vice versa.
Paper proposes SCott optimizer to reduce forecasting model training variance.
problem Large variance in gradient estimation for forecasting models.
method Stratified sampling and control variate to reduce gradient variance.
result SCott optimizer converges faster on time series forecasting problems.
Mamba improves time series forecasting by quantifying uncertainty.
problem Mamba forecasts have high mean errors in benchmarks.
method Dual-network framework for probabilistic forecasting.
result Predictive uncertainty reduced significantly for both synthetic and real-world data.
Proposes a modified Morgan-Pitman test for evaluating variances in machine learning models.
problem Limited ability to account for sampling variability in model selection.
method Enhances the classic Morgan-Pitman test for robustness in non-linear models with heavy-tailed distributions or outliers.
result Demonstrates the test's effectiveness and practical utility in model evaluation and selection.
Investigates Bitcoin market risk, showing volatility and jumps impact future volatility.
problem Understanding and forecasting the risk dynamics of Bitcoin market.
method Comprehensive investigation using realized volatility and jumps analysis.
result Jumps, especially positive ones, reduce future realized variance; long-term realized variance benefits from modeling jumps.
This study investigates how Decision-Focused Learning improves stock return predictions for better portfolio optimization.
problem The challenge of precise expected returns estimation in mean-variance optimization.
method Investigates Decision-Focused Learning (DFL) to adjust stock return prediction models for MVO.
result DFL tilts prediction errors by the inverse covariance matrix, leading to systematic prediction biases in portfolio optimization.
Study optimizes pricing under uncertainty and capacity constraints.
problem Optimizing pricing decisions under demand uncertainty and capacity constraints.
method Analyzes linear demand, stochastic noise, and finite capacity; uses certified demand forecasts and control variates.
result Certified demand forecasts reduce regret from O ( T ) O(\sqrt{T}) O ( T ) to O ( log T ) O(\log T) O ( log T ) under certain conditions. Unified kernel for prediction markets reduces belief variance forecast error.
problem Lack of standardized tools for quoting and hedging belief risk in prediction markets.
method Logit jump-diffusion model with risk-neutral drift, calibration pipeline, and coherent derivative layer.
result Model reduces forecast error compared to diffusion-only and probability-space baselines.
State-space models win a forecasting competition for unstable data.
problem Forecasting electricity demand during the post-covid period.
method Adapting state-space models to balance time-series adaptability and machine learning complexity.
result State-space models provide a better compromise between adaptability and accuracy for non-stationary data.
Develops likelihood-based methods for trawl processes, improving forecasting accuracy.
problem Statistical modeling of trawl processes with heavy tails and long memory.
method Composite likelihood estimation as a stochastic optimization problem, using gradient descent methods.
result New gradient estimators with significantly reduced variance for trawl processes.
The paper compares machine learning models for forecasting residential gas demand, highlighting the impact of temperature forecasts.
problem Forecasting residential gas demand for optimal energy planning.
method Implemented and compared five models: Ridge Regression, GP, k-Nearest Neighbour, ANN, and Torus Model.
result ANN is the best model in terms of RMSE, while GP is the best in terms of MAE.
Study forecasts vegetable prices in Nepal using a novel index and ensemble model.
problem High volatility and cultural influences on agricultural commodity prices.
method Developed KVPI, created features, evaluated multiple models, introduced Momentum-Corrected Online Stacking Ensemble.
result Achieved RMSE of 1.771, MAPE of 0.68%, and R-squared of 0.845 at 90-day horizon.
Forecast reconciliation improves portfolio risk forecasts, especially when true covariance is known.
problem Improving portfolio risk forecasts using multivariate GARCH models.
method Combining univariate and multivariate forecasts with forecast reconciliation techniques.
result Forecast reconciliation improves over standard multivariate approaches, especially when true covariance is known.
A new framework detects forecast model inadequacies using online monitoring of forecast errors.
problem Inaccurate forecasts lead to poor decision-making in complex models.
method Sequential changepoint techniques on forecast errors for real-time identification of process changes.
result The framework identifies shifts in forecast errors faster than in the original models, indicating process changes.
Paper develops a robust hedging framework to reduce market risk and uncertainty.
problem Managing uncertainty and risk exposure in portfolio management.
method Combines high-frequency realized variance, covariance measures, and autoregressive models for multi-step volatility forecasting. Uses a box-uncertainty robust optimization scheme to derive a closed-form solution for the robust hedge ratio.
result Robust hedge ratios are more stable and entail lower turnover than standard dynamic hedges, improving downside protection and risk-adjusted performance.
Forecast stock return distributions using neural networks.
problem Accurately modeling non-Gaussian stock return features.
method Two-stage quantile neural network with spline interpolation.
result Improved mean and variance forecasts compared to standard models.
Study introduces a new investment strategy model using lazy factor and probability weights.
problem Optimizing investment strategies in volatile markets with transaction costs.
method Combines Price Portfolio Forecasting and Mean-Variance Models with Transaction Costs, using probability weights as laziness factor coefficients.
result Model demonstrates adaptability and generalizability in transforming investment strategies.
A TTA framework improves forecasting accuracy in non-stationary time series.
problem Improving forecasting accuracy in non-stationary time series.
method Normalization-based test-time adaptation for causal timeseries forecasting and direction classification.
result Normalization-based TTA improves forecasting error in synthetic gradual drift and can even hurt in aggressive norm-only adaptation in financial markets.
Study finds sales forecasters overreact to extreme news.
problem Understanding how forecasters react to sales growth news.
method Proposes a framework with fat-tailed dynamics and linear forecasting rule.
result Forecasters overreact to significant sales growth news.
The study compares MS-GARCH and SARV models for Bitcoin volatility forecasting.
problem Analyzing Bitcoin price volatility using Markov Switching-GARCH and SARV models.
method Examined Markov Switching-GARCH and SARV models, comparing their forecasting performance.
result SARV models outperform MS-GARCH models in Bitcoin volatility forecasting.
Enhances traffic forecasting with dynamic regression incorporating error modeling.
problem Improving accuracy of traffic forecasts using deep spatiotemporal models.
method Integrates matrix-variate autoregressive (AR) model into loss function for error series of base model.
result Improved traffic forecasting performance on SOTA models with interpretable AR coefficients.
Enhances portfolio construction with tailored regime forecasts for individual assets.
problem Traditional portfolio construction methods fail to account for asset-specific market conditions.
method Hybrid framework combining unsupervised and supervised learning for regime identification and forecasting.
result Outperforms traditional portfolio models across various asset classes.
CD estimates demand by separating promotions from a stable base, reducing inventory costs.
problem Operational volatility in demand forecasts leads to excessive safety stock and inventory costs.
method Contextual Deconvolution (CD) decomposes demand into a smooth base and promotion-driven shocks.
result CD reduces inventory costs by stabilizing demand forecasts, but under-provisions event spikes.
RPNN-EOFs model improves time series forecasting accuracy.
problem Improving time series forecasting accuracy for complex systems.
method Combines higher-order neural networks with error-output feedbacks.
result RPNN-EOFs outperformed other models in forecasting the Mackey-Glass time series.
Method improves volatility targeting for index construction.
problem High turnover, leverage spikes, and sensitivity to estimation error in existing volatility-targeting strategies.
method Proportional-control approach for setting index weights that corrects tracking error through feedback.
result The proportional-control approach achieves the target volatility more effectively than open-loop alternatives.
PhI-GPR improves power grid state estimation and forecasting.
problem Accurate state estimation and forecasting in power grids with sparse measurements.
method Physics-informed Gaussian process regression (PhI-GPR) for stochastic differential equations.
result PhI-GPR provides more accurate forecasts and estimates of power grid states compared to ARIMA.
Paper introduces probabilistic forecasting methods for cryptocurrency volatility.
problem Inadequate point forecasting methods for capturing full spectrum of volatility outcomes.
method Combines multiple base models (statistical and machine learning) to estimate conditional quantiles of cryptocurrency realized variance.
result QRS method outperforms sophisticated alternatives for Bitcoin volatility forecasting.
CNN improves medium-range temperature forecasts with limited resources.
problem Limited computational resources for high-resolution temperature forecasts.
method CNN post-processing with ensemble NWP models for bias correction and spatial downscaling.
result High-resolution (5-km) surface temperature forecasts with lead times up to 5.5 days.
A hybrid strategy forecasts short-term loads using Warm-start Gradient Tree Boosting.
problem Lack of effective short-term load forecasting methods.
method Hybrid strategy integrating four different inference models: tree-based ensemble method Warm-start Gradient Tree Boosting (WGTB).
result Demonstrates effectiveness of hybrid strategy on real datasets.
Simple proof shows forecasts can be calibrated in a few periods.
problem Ensuring forecasts are calibrated over multiple periods.
method Uses minimax theorem to prove existence and guarantees calibration error.
result Calibration can be achieved in N 3 N^3 N 3 periods with error at most 1 / N 1/N 1/ N . Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improv…
The realized GARCH framework is extended to incorporate the two-sided Weibull distribution, for the purpose of volatility and tail risk forecasting in a financial time series. Further, the realized range, as a competitor for realized variance or daily returns, is employed in the realized GARCH framework. Further, sub-s…
The paper forecasts corporate distress using a novel MIDAS logistic regression method.
problem Forecasting corporate distress with right-censored data, high-dimensional predictors, and mixed-frequency data.
method The paper introduces a novel high-dimensional censored MIDAS logistic regression method that handles censoring through inverse probability weighting and employs a sparse-group penalty for mixed-frequency predictors.
result The method achieves accurate estimation and superior performance in predicting financial distress of Chinese-listed firms.
HSR reduces analyst earnings forecast errors by lowering travel friction.
problem How HSR connectivity affects analyst earnings forecast errors in China.
method Firm-year panel data from 2008-2019; placebo test to rule out pre-existing trends.
result HSR reduces analyst earnings forecast errors after connectivity, not before.