arXiv research

Analog forecasting uses local dynamics to predict chaotic systems.

Analog methods improve forecast accuracy in complex models.

Kernel analog forecasting studied for multiscale systems.

New method interprets machine learning forecasts as historical analogies.

The Analog Ensemble (AnEn) method tries to estimate the probability distribution of the future state of the atmosphere with a set of past observations that correspond to the best analogs of a deterministic Numerical Weather Prediction (NWP). This model post-processing method has been successfully used to improve the fo…

The paper proposes a method to improve forecast combination accuracy using portfolio theory.

A phase plot of the oil economy is built using the literature data of world oil production, price, and EROEI (Energy Returned on Energy Invested). An analogy between the oil economy and the Benard convection is proposed; some methods of interpretation and forecast of the system behavior are also shown based on "phase p…

LLM forecasting benchmarks suffer from information leakage, which confounds model performance.

Rainfall ensemble forecasts have to be skillful for both low precipitation and extreme events. We present statistical post-processing methods based on Quantile Regression Forests (QRF) and Gradient Forests (GF) with a parametric extension for heavy-tailed distributions. Our goal is to improve ensemble quality for all t…

The paper evaluates the probability distributions of analog-to-target distances for multiple analogs.

The paper develops a method to forecast financial risk multiple steps ahead using quantile time series and historical simulation.

We propose a new approach for properly analyzing stochastic time series by mapping the dynamics of time series fluctuations onto a suitable nonequilibrium surface-growth problem. In this framework, the fluctuation sampling time interval plays the role of time variable, whereas the physical time is treated as the analog…

The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. While model-driven representations remain the classic approaches, data-driven representations become more and more appealing to benefit from available large-scale observation and simulation dat…

ContraSim learns financial headline similarities for market forecasting.

LaT-PFN model predicts time series with zero-shot capability.

We establish optimal rates for online regression for arbitrary classes of regression functions in terms of the sequential entropy introduced in (Rakhlin, Sridharan, Tewari, 2010). The optimal rates are shown to exhibit a phase transition analogous to the i.i.d./statistical learning case, studied in (Rakhlin, Sridharan,…

We introduce a new loss function for evaluating forecasts and estimate models using it.

Optimal trading patterns adjust based on market efficiency and slippage costs.

A new model framework called Realized Conditional Autoregressive Expectile (Realized-CARE) is proposed, through incorporating a measurement equation into the conventional CARE model, in a manner analogous to the Realized-GARCH model. Competing realized measures (e.g. Realized Variance and Realized Range) are employed a…

The paper studies the distance from calibration in sequential prediction, proving upper and lower bounds.

Optimal reconciliation keeps some forecasts unchanged in hierarchical forecasting.

Bob predicts a future observation based on a sample of size one. Alice can draw a sample of any size before issuing her prediction. How much better can she do than Bob? Perhaps surprisingly, under a large class of loss functions, which we refer to as the Cover-Hart family, the best Alice can do is to halve Bob's risk. …

Short-term load forecasting is a critical element of power systems energy management systems. In recent years, probabilistic load forecasting (PLF) has gained increased attention for its ability to provide uncertainty information that helps to improve the reliability and economics of system operation performances. This…

Combining forecasts of 16 ED causes improves accuracy and stability.

Paper extracts features from time series to improve forecasting accuracy.

Conditional forecasts improve performative prediction accuracy.

Following the thermodynamic formulation of multifractal measure that was shown to be capable of detecting large fluctuations at an early stage, here we propose a new index which permits us to distinguish events like financial crisis in real time . We calculate the partition function from where we obtain thermodynamic q…

Study improves seasonal forecasts using deep learning.

This dissertation explores the integration of learning and analogy-making through the development of a computer program, called Analogator, that learns to make analogies by example. By "seeing" many different analogy problems, along with possible solutions, Analogator gradually develops an ability to make new analogies…

Two new methods improve forecasting of functional time series data.

Deep learning improves time series forecasting, outperforming other methods.

Nowadays, with the unprecedented penetration of renewable distributed energy resources (DERs), the necessity of an efficient energy forecasting model is more demanding than before. Generally, forecasting models are trained using observed weather data while the trained models are applied for energy forecasting using for…

MPANF improves naive forecast by incorporating directional information.

Simplifies forecast combination by using diversity of out-of-sample forecasts.

Develops forecast hedging for improved calibration of forecasts.

Microdata improves inflation forecasts after major shocks, study finds.

Improved forecast accuracy for Knitwear by 20% using adaptive AI/ML model.

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…

Paper proposes a new method for selecting the best hierarchical forecasting approach.

Proposes a neural network for accurate and reconciled hierarchical time series forecasting.

The key contribution of this paper is to propose a classification into two dimensions of the load forecasting studies to decide which forecasting tools to use in which case. This classification aims to provide a synthetic view of the relevant forecasting techniques and methodologies by forecasting problem. In addition,…

This paper reviews forecast combinations over 50 years, highlighting their evolution and utility.

This paper improves forecast stability without sacrificing accuracy using dynamic loss weighting.

A new framework detects forecast model inadequacies using online monitoring of forecast errors.

Archive of 20 time series datasets for forecasting evaluation.

As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model w…

Develops a method to ensure accurate quantile forecasts across multiple levels.

A new hierarchical forecasting method using machine learning improves forecast accuracy.