Smooth calibration improves forecast reliability even with leaked information.
problem Improving forecast reliability with leaked information.
method Combining nearby forecasts to ensure smooth calibration, which can be guaranteed by deterministic procedures.
result Smooth calibration can be guaranteed by deterministic procedures even with leaked forecasts, and it yields uncoupled finite-memory dynamics in games.
Exponentially smoothed RNNs improve industrial forecasting.
problem Complexity and non-stationarity in industrial time series data.
method Exponential smoothed recurrent neural networks (RNNs) for modeling non-linear dynamics.
result Exponentially smoothed RNNs outperform traditional models in multi-step forecasting.
A winning method for day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
problem Day-ahead electricity demand forecasting during and after the COVID-19 pandemic.
method Online forecast combination of multiple point prediction models with a holiday adjustment procedure and smoothed Bernstein Online Aggregation (BOA).
result Excellent forecasting performance, particularly due to the holiday adjustment procedure and fully adaptive smoothed BOA approach.
Algorithm combines expert forecasts for long-term time series prediction.
problem Long-term time series prediction with expert advice.
method Develops algorithms to combine expert forecasts for long-term prediction, proving adversarial regret bounds.
result Obtains smoothing mechanism to protect against trend changes, noise, and outliers.
Proposes time-smoothed gradients for more stable online forecasting.
problem Stability and efficiency in online forecasting with SGD.
method Introduces time-smoothed gradients within SGD update rules.
result Time-smoothed gradients yield more stable results than existing methods.
Proposes a new model using exponential smoothing cells for robust time series analysis.
problem Challenges of traditional exponential smoothing in noisy data and changing series.
method Flexible model using exponential smoothing cells for overlapping time windows, solving a structured convex optimization problem.
result Can detect and remove outliers, denoise data, fill in missing observations, and provide meaningful forecasts.
Improved sales forecasting at various levels using ensemble methods.
problem Enhancing sales forecasting accuracy at different levels of e-commerce data.
method Hierarchical robust aggregation of sales forecasts using exponential smoothing and Holt's linear trend method.
result Better forecasts at subsubfamily, subfamily, and family levels compared to individual techniques.
Paper improves probabilistic forecasts of electricity prices.
problem Combining multivariate probabilistic forecasts with dependencies.
method Online learning algorithm using Bernstein Online Aggregation (BOA).
result Significant improvement in CRPS over uniform combination.
Method predicts crime hotspots with high resolution.
problem Forecasting sparse spatiotemporal events like crime.
method Combines RKHS methods with autoregressive smoothing kernels.
result Significantly outperforms baseline models for sparse events.
This research improves LSTM for monthly electricity demand forecasting using pattern-based methods.
problem Forecasting mid-term monthly electricity demand with high accuracy.
method Developed a hybrid LSTM model using x-patterns and exponential smoothing.
result The hybrid model outperformed standard LSTM and classical models.
Study compares local and global models for hierarchical forecasting accuracy.
problem Challenges in hierarchical time series forecasting, especially in accuracy and information utilisation.
method Developed and evaluated local and global forecasting models (GFMs) to exploit cross-series and cross-hierarchies information.
result Global Forecasting Models (GFMs) outperform local models in hierarchical forecasting accuracy and computational efficiency.
DMIDAS improves long-term forecasting accuracy in healthcare and electricity data.
problem Challenging long-term forecasting accuracy and computational complexity.
method Smoothness regularization and mixed data sampling techniques integrated into NBEATS architecture.
result Improves prediction accuracy by 5% on long forecasting horizons (1000 timestamps) compared to state-of-the-art models.
Develops theory for data-driven methods in dynamical systems.
problem Lack of analysis for data-driven methods in dynamical systems.
method Establishes existence of mapping and properties of operator learning architecture.
result Novel universal approximation theorems for smoothing and forecasting.
Bayesian method speeds up demand forecasting for e-commerce.
problem Demand forecasting for fast and bursty items at scale.
method Approximate Bayesian inference using Newton-Raphson algorithm and Kalman smoothing.
result Significantly outperforms competing approaches on large datasets.
SQR Averaging improves probabilistic electricity price forecasting.
problem Accurate short-term price forecasting in electricity markets.
method Smoothing Quantile Regression Averaging.
result SQR Averaging leads to profit increases of up to 3.5% in day-ahead power trading.
New model predicts energy prices volatility by smoothing time variation and persistence.
problem Separate study of volatility's time variation and persistence.
method Dynamic persistence model that allows shocks with heterogeneous persistence to vary smoothly over time.
result Significantly improves volatility forecasts over state-of-the-art models.
Improved Gibbs sampler speeds up Bayesian exponential smoothing model.
problem Computational inefficiency of original NUTS sampler.
method Modifications to the original model and a bespoke Gibbs sampler.
result Significant improvement in sampling time by an order of magnitude.
Paper presents a novel neural network for probabilistic wind power forecasting.
problem Improving decision making in smart power grids with renewable energy sources.
method Combines smooth pinball loss function with neural network architecture and weighting scheme.
result Proposed method outperforms benchmarks in probabilistic wind power forecasting.
Hybrid model combines LSTM and ETS for mid-term electric load forecasting.
problem Mid-term electric load forecasting accuracy.
method Combines LSTM, ETS, and ensemble learning; uses dilated LSTM for long-term relationships.
result High performance and competitiveness compared to classical and machine learning models.
MES-LSTM hybrid method improves multivariate time series forecasting and mortality modeling.
problem Challenges in applying hybrid forecast methods to multivariate data.
method Generalized multivariate extension of ES-RNN, utilizing vectorized implementation.
result MES-LSTM shows significant improvement over pure statistical and deep learning methods in forecast accuracy and prediction interval construction.
New methods for scoring function decomposition improve forecast evaluation.
problem Improving forecast evaluation and understanding forecast components.
method Linear recalibration of forecasts for miscalibration, discrimination, and uncertainty.
result Enhanced statistical power and deeper insights into forecast components.
New method combines simple forecasting techniques for river flow predictions.
problem Improving accuracy of long-term hydrological forecasts.
method Combines at least two forecasting methods using median combiner.
result Performs well in long-term forecasts, especially with multiple methods.
We introduce a new local regret framework for non-convex models in dynamic environments.
problem Challenges in online forecasting for non-convex models with frequent updates and concept drift.
method We propose a novel local regret framework and a time-smoothed gradient update rule.
result Our approach yields more stable, robust, and computationally efficient forecasting compared to state-of-the-art methods.
FCNv2 robustness tested under noise and random initial conditions.
problem Assessing AI weather forecasting model robustness to input noise.
method Two experiments with varying noise levels and random initial conditions.
result FCNv2 preserves hurricane features under low to moderate noise, but underestimates intensity and persistence.
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.
Combines spline interpolation and ARIMA for stock market forecasting.
problem Limited predictive performance of ARIMA in noisy data.
method Integrates cubic spline interpolation and ARIMA for time series forecasting.
result Demonstrates guidance for short-term stock market forecasting.
We introduce tools for inference in the multifractal random walk introduced by Bacry et al. (2001). These tools include formulas for smoothing, filtering and volatility forecasting. In addition, we present methods for computing conditional densities for one- and multi-step returns. The inference techniques presented in…
Proposes DILATE and STRIPE++ for precise time series forecasting.
problem Non-stationary signals with sudden changes.
method Incorporates shape and temporal criteria in deep learning models.
result Improves precision in deterministic and probabilistic forecasting.
Polynomial-time algorithm forecasts TV-bounded sequences with optimal error rate.
problem Online forecasting of sequences with bounded total variation under noisy observations.
method Designing an O ( n log n ) O(n\log n) O ( n log n ) -time algorithm leveraging Haar wavelet basis and adaptivity. result Achieves optimal O ( n 1 / 3 ) O(n^{1/3}) O ( n 1/3 ) cumulative square error with high probability. 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.
Simple 1D-CNN network predicts electricity loads 36 hours ahead.
problem Forecasting electricity loads for future time periods.
method Used a one-dimensional CNN with parameter scanning to optimize kernel size, filters, and dense size.
result Good forecast quality achieved with basic CNN architectures.
New model predicts time series quantiles for nonstationary data.
problem Nonparametric probabilistic forecasting of nonstationary univariate time series.
method Composite Quantile Fourier Neural Network (QFNN) for extrapolation-based nonlinear quantile regression.
result Effective in providing high quality and accurate probabilistic predictions.
Multivariate boosted trees improve forecasting and control by capturing correlated predictions.
problem Capturing multivariate target cross-correlations and applying structured penalties to predictions.
method A computationally efficient algorithm for fitting multivariate boosted trees.
result Multivariate trees outperform univariate counterparts in correlated prediction scenarios.
Improved price forecasting using Kalman filters reduces trading hiccups.
problem Sudden price whipsaws causing trading losses.
method Kalman filter theory and pseudo code implementation.
result Kalman filter models outperform traditional moving averages in price forecasting.
New algorithm reduces prediction errors across various loss functions.
problem Online forecasting algorithms' inability to adapt to different loss functions.
method Design of a novel Follow-the-Perturbed-Leader (FTPL) algorithm with self-concordant noise.
result Simultaneously achieves i l d e O ( T ) ilde O(\sqrt{T}) i l d e O ( T ) regret for bounded proper losses and O ( log T ) O(\log T) O ( log T ) regret for bounded smooth proper losses. 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.
Introduces recency bias to improve time-series forecasting.
problem Lack of recency bias in standard Transformer attention for time-series data.
method Reweights attention scores with a smooth heavy-tailed decay to emphasize nearby observations.
result Recency-biased attention consistently improves sequential modeling and achieves competitive performance on time-series forecasting benchmarks.
Develops ML tool for macroeconomic forecasting with clear interpretations.
problem Forecasting and understanding macroeconomic parameters over time.
method Macroeconomic Random Forest (MRF) algorithm, Generalized Time-Varying Parameters (GTVPs).
result Clear forecasting gains and accurate predictions of unemployment and inflation.
New calibration measure SSCE ensures truthful prediction, unlike existing measures.
problem Ensuring truthful calibration measures in sequential prediction.
method Introduced a new calibration measure, Subsampled Smooth Calibration Error (SSCE).
result SSCE ensures truthful prediction, while existing measures are far from truthful.
Simple models outperformed sophisticated ones in forecasting Turkish lira exchange rates.
problem Forecasting Turkish lira exchange rates through univariate techniques.
method Used several models including simple exponential smoothing to predict daily exchange rates.
result Simple exponential smoothing model outperformed all other alternatives.
Bayesian model predicts evolving guest origin markets in tourism.
problem Forecasting the changing composition of guest origin markets in tourism.
method Developed and applied Bayesian Dirichlet autoregressive moving average (BDARMA) models to Airbnb booking data.
result BDARMA models achieve lower forecast error and competitive performance in guest origin market shares.
LSTM outperforms ARIMA in forecasting time series data.
problem Comparing traditional ARIMA models with deep learning LSTM for time series forecasting.
method Empirical studies using LSTM vs. ARIMA models.
result LSTM outperforms ARIMA by 84-87% in reducing error rates.
Bayesian models predict evolving guest origin markets in tourism.
problem Forecasting the changing composition of guest origin markets in tourism.
method Developed and applied Bayesian Dirichlet autoregressive moving average (BDARMA) models to Airbnb booking data.
result BDARMA models outperform standard benchmarks in forecasting guest origin market shares.
EnCF improves data assimilation for implicit, non-smooth observations.
problem Data assimilation challenges with implicit, many-to-one observations.
method EnCF uses a stochastic controlled flow to update forecast distributions.
result EnCF outperforms Kalman filters for non-Gaussian, implicit observations.
PhysicsFormer improves TSF models for GSWF with WEATHER-5K dataset.
problem Lack of comprehensive datasets for GSWF.
method PhysicsFormer combines dynamic core and Transformer, enforcing physical consistency.
result PhysicsFormer outperforms TSF models in operational forecasting.
Advanced forecasting models outperform Holt-Winters and ARIMA for stock market data.
problem Forecasting stock market data with improved accuracy.
method Developed 24 two-parameter families of forecasting functions using a nonparametric approach.
result Our models outperform Holt-Winters and ARIMA in terms of lower sum of absolute errors and higher number of accurate forecasts.
Model forecasts water demand with probabilistic multi-step-ahead approach.
problem Accurate probabilistic forecasts of water demand for operational control.
method Time series model with Lasso for high-dimensional feature space.
result Accurate, interpretable, and fast computable forecasting model.
The accuracy of the household electricity consumption forecast is vital in taking better cost effective and energy efficient decisions. In order to design accurate, proper and efficient forecasting model, characteristics of the series have to been analyzed. The source of time series data comes from Online Enerjisa Syst…