A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Two new methods improve forecasting of functional time series data.
Market forecasts converge to true values if some agents are correct.
Low-rank forecasting improves consistency in time series predictions.
The paper proposes a method to improve forecast combination accuracy using portfolio theory.
GraphSVR forecasts urban air pollution robustly across stations and seasons.
BAVART model combines VAR and BART for non-linear forecasting.
The participants of the electricity market concern very much the market price evolution. Various technologies have been developed for price forecast. SVM (Support Vector Machine) has shown its good performance in market price forecast. Two approaches for forming the market bidding strategies based on SVM are proposed. …
The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from …
An accurate load forecasting has always been one of the main indispensable parts in the operation and planning of power systems. Among different time horizons of forecasting, while short-term load forecasting (STLF) and long-term load forecasting (LTLF) have respectively got benefits of accurate predictors and probabil…
Novel TM-vector model predicts stock market direction using Twitter and market data.
Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision-making processes in the smart power grid for better integrating renewable energies such as wind. Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in …
The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singul…
The paper develops fast Bayesian methods for estimating huge PVARs with competitive forecasts.
Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short an…
Proposes a new model to maximize out-of-sample Sharpe ratios by forecasting tangency portfolios.
This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recast accuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all sevent…
Deep models predict intraday electricity prices accurately.
This paper compares traditional econometric and contemporary machine/deep learning techniques for forecasting foreign exchange rates.
Paper uses non-linear dimension reduction for better economic forecasting.
With the widespread engineering applications ranging from artificial intelligence and big data decision-making, originally a lot of tedious financial data processing, processing and analysis have become more and more convenient and effective. This paper aims to improve the accuracy of stock price forecasting. It improv…
Forecast dam inflow using sea surface feature weights.
DeepMIDE forecasts wind speeds across space, time, and height for offshore wind energy.
Application of fuzzy support vector machine in stock price forecast. Support vector machine is a new type of machine learning method proposed in 1990s. It can deal with classification and regression problems very successfully. Due to the excellent learning performance of support vector machine, the technology has becom…
This paper improves SVM prediction uncertainty quantification methods.
Microdata improves inflation forecasts after major shocks, study finds.
Cryptocurrency prices predicted using LSTM, SVM, and polynomial regression.
mfBm models and forecasts volatility with different Hurst exponents and correlations.
Study forecasts Bitcoin prices using ML algorithms.
QBVAR improves oil price forecasting across quantiles, especially for downside risk.
Proposes MVG-CRPS for robust multivariate forecasting.
Motion Code models time series dynamics with sparse approximations.
Paper distills ensemble ENSO forecasts into simpler models for better diagnostics.
Support Vector Regression (SVR) has achieved high performance on forecasting future behavior of random systems. However, the performance of SVR models highly depends upon the appropriate choice of SVR parameters. In this study, a novel BOA-SVR model based on Butterfly Optimization Algorithm (BOA) is presented. The perf…
For the prediction with experts' advice setting, we construct forecasting algorithms that suffer loss not much more than any expert in the pool. In contrast to the standard approach, we investigate the case of long-term forecasting of time series and consider two scenarios. In the first one, at each step the learne…
Generative model learns functional vector fields for pharmacokinetics.
A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilber…
Improved covariance matrix forecasting for S&P 500 using factor models and shrinkage.
Improved genetic algorithm optimizes SVR for robust long-term stock index forecasting.
Improved forecasting for irregularly-sampled time series using kernel flows.
Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series. However, as the number of component series is increased, the VAR model becomes overparameterized. Several authors have addressed this issue by incorporating regularized approaches, such as the lasso in VAR estimation. Traditional a…
The paper introduces a machine learning method to forecast market direction using efficient frontier coefficients.
Enhances FAVAR models with autoencoder for better economic forecasting and interpretability.
One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for forecasting, for example, by the dynamic factor models. A challenging issue is to determine which variables and (their)…
Sparse Tucker decomposition with graph regularization improves time series forecasting accuracy.
Incorporating nonlinearity is paramount to predicting the future states of a dynamical system, its response to shocks, and its underlying causal network. However, most existing methods for causality detection and impulse response, such as Vector Autoregression (VAR), assume linearity and are thus unable to capture the …
Point forecasting of univariate time series is a challenging problem with extensive work having been conducted. However, nonparametric probabilistic forecasting of time series, such as in the form of quantiles or prediction intervals is an even more challenging problem. In an effort to expand the possible forecasting p…
Solar forecasting accuracy is affected by weather conditions, and weather awareness forecasting models are expected to improve the performance. However, it may not be available and reliable to classify different forecasting tasks by using only meteorological weather categorization. In this paper, an unsupervised cluste…
Paper presents a method for imputing and forecasting structural response from incomplete sensor data.