Two new methods improve forecasting of functional time series data.
problem Forecasting of functional time-dependent data.
method Functional Singular Spectrum Analysis (FSFA) based forecasting methods.
result Our methods outperform existing algorithms for periodic stochastic processes.
Archive of 20 time series datasets for forecasting evaluation.
problem Lack of comprehensive time series forecasting datasets.
method Compilation and characterisation of 20 datasets from various domains.
result Characterisation and performance evaluation of datasets.
Proposes a neural network for accurate and reconciled hierarchical time series forecasting.
problem Forecasting and reconciling hierarchical time series data.
method Uses a deep neural network to directly produce accurate and reconciled forecasts, minimizing a customized loss function at training time.
result Our approach outperforms state-of-the-art competitors in hierarchical forecasting on real-world datasets.
New method forecasts values and timing in irregular time series.
problem Forecasting values and timing in sparse, irregularly sampled multivariate time series.
method Proposes a novel approach for forecasting values and timing in irregular time series.
result Successfully forecasts values and timing in irregular time series.
EasyTime simplifies time series forecasting for researchers and practitioners.
problem Ease of use and accuracy in time series forecasting.
method One-click evaluation, automated ensemble, natural language Q&A.
result Superior forecasting accuracy compared to individual methods.
Study compares forecasting methods for logistics time series.
problem Improving forecasting accuracy in logistics.
method Compared statistical and machine learning methods on simulated time series.
result Statistical methods outperformed machine learning in one-step forecasts.
For2For combines forecasts to improve time series forecasting.
problem Improving time series forecasting accuracy.
method Combines standard forecasting methods and machine learning models using forecasts as features.
result Outperforms all submissions in the M4 competition for quarterly series and most monthly series.
Recurrent neural networks improve time series forecasting accuracy.
problem Time series forecasting is challenging, especially for sequential data.
method A recurrent neural network framework for feature engineering, prediction, and evaluation is presented.
result The LSTM and GRU networks outperform traditional methods in forecasting accuracy.
This paper improves forecasts for diverse time series by averaging similar ones.
problem Forecasting challenges in heterogeneous time series.
method Dynamic Time Warping to find similar time series, k-Nearest Neighbor averaging.
result Averaging improves forecasts of simple models.
Automatically extracts features from time series data for improved forecasting.
problem Manual feature selection for time series forecasting is inefficient and prone to errors.
method Extracts features from time series using recurrence plots and computer vision algorithms.
result Automatically extracted features lead to highly comparable and sometimes superior forecasting performance.
A method to improve time series forecasting by dynamically adjusting weights of forecasters.
problem Challenges in time series forecasting due to evolving data distributions.
method Dynamic re-weighting of forecasters based on evolving data distributions.
result Competitive performance compared to state-of-the-art methods for combining forecasters.
Improved forecasting in daily time series competition using a correlator method.
problem Forecasting daily time series with data leakage issues.
method Ensemble of five statistical forecasting methods and a correlator method.
result The correlator method was responsible for most of the gains over naive forecasting.
Deep learning improves time series forecasting, outperforming other methods.
problem Improving time series forecasting accuracy.
method Deep learning models for time series prediction.
result Deep learning models consistently outperform other methods in forecasting competitions.
New method uses Transformers for flu forecasting.
problem Forecasting influenza-like illness trends.
method Transformer-based machine learning models with self-attention.
result Forecasting results are competitive with state-of-the-art methods.
Few-shot learning improves time-series forecasting with limited data.
problem Limited data in target tasks degrade forecasting performance.
method A few-shot learning method using recurrent neural networks with attention.
result The model forecasts future values effectively with minimal data.
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.
The paper compares clustering methods for improving time series forecasting accuracy.
problem Improving time series forecasting accuracy using neural networks.
method Investigates feature-based and distance-based clustering methods for time series forecasting.
result Feature-based clustering outperforms distance-based clustering in terms of speed and efficiency.
Automated energy forecasts using open data.
problem Lack of timely and accurate energy forecasts.
method Automated forecasting framework using open access data.
result Comparable prediction accuracy to country-provided estimates.
Paper proposes a new method for selecting the best hierarchical forecasting approach.
problem Selecting the best method for reconciling base forecasts in hierarchical time series.
method Conditional hierarchical forecasting using machine learning and time series features.
result Conditional hierarchical forecasting leads to significantly more accurate forecasts, especially at lower levels.
Meta-GLAR combines global deep representations with local adaptation for improved forecasting accuracy.
problem Joint learning from related time series boosts accuracy but fails for out-of-sample forecasting.
method Meta-GLAR uses a meta-learning approach to adapt RNN representations for each time series.
result Meta-GLAR outperforms state-of-the-art methods in out-of-sample forecasting accuracy.
Meta-learning selects best time series forecasting method.
problem Selecting the best time series forecasting method.
method Meta-learning approach using a pool of forecasting algorithms and feature selection.
result Meta-learners outperformed individual forecasters and improved performance with feature selection.
New method uses shared attention for multi-task time series forecasting.
problem Insufficient training instances in single-task forecasting.
method Self-attention based sharing schemes across multiple tasks.
result Outperforms state-of-the-art single-task forecasting baselines and RNN-based multi-task forecasting method.
Optimal model selection for forecasting large collections of short time series using latent space.
problem Challenges in choosing among multiple forecasting methods for large, high-dimensional time series with limited data.
method Combining low-rank temporal matrix factorization with optimal model selection using cross-validation.
result Forecasting latent factors leads to significant performance gains compared to direct uni-variate model application.
Deep state space model forecasts time series with uncertainty.
problem Probabilistic forecasting for risk management.
method Parameterized deep networks for non-linear models, recurrent neural nets for dependency, ARD network for exogenous variables.
result Accurate and sharp probabilistic forecasts with realistic uncertainty growth.
MPANF improves naive forecast by incorporating directional information.
problem Challenging to surpass naive forecast in financial time series.
method Combines naive forecast with movement prediction and accuracy.
result MPANF generally outperforms common benchmarks.
Model uses RNNs to forecast similar time series groups.
problem Forecasting similar time series databases with traditional methods.
method Time series clustering and LSTM networks.
result Outperforms baseline LSTM model and other methods in forecasting competitions.
Deep neural networks improve forecasting of non-stationary time-series data.
problem Forecasting non-stationary time-series data with structural breaks and high volatility.
method Evaluation of DNN models including MLP, CNN, LSTM-RNN, and GRU-RNN on 10 Indian financial stocks.
result DNN models show better performance for single-step forecasting but degrade for multi-step forecasting, especially for long forecast periods.
Unified framework for integrating linear constraints in time series forecasting.
problem Challenges in traditional time series forecasting algorithms.
method Unified framework combining linear constraints in time series forecasting.
result Exact minimizer of the constrained empirical risk can be computed efficiently using linear algebra.
Meta-learning predicts optimal ensemble size and methods for time series forecasting.
problem Finding the best ensemble of time series forecasting methods.
method Two-step approach using meta-learning to predict ensemble size and methods.
result Meta-learning outperformed benchmarks in forecasting errors for all data types and horizons.
This paper extends forecast reconciliation to non-linearly constrained time series.
problem Forecasting time series with non-linear constraints.
method Non-linearly Constrained Reconciliation (NLCR) algorithm that adjusts forecasts to meet non-linear constraints.
result NLCR significantly improves forecast accuracy compared to benchmarks.
Improved prediction of hierarchical time series using structured regularization.
problem Making coherent forecasts for hierarchical time series.
method Structured regularization method for bottom-level time series predictions.
result Superior prediction accuracy and computational efficiency compared to previous methods.
CVAE improves stock volume forecasting with advanced input variables.
problem Improving accuracy of daily stock volume forecasts.
method Conditional Variational Auto-Encoder (CVAE) with advanced input variables.
result CVAE generates non-linear forecasts with better accuracy and correlation to actual data.
Proposes a new algorithm for efficient probabilistic reconciliation of forecasts.
problem Ensuring coherence in forecasts for hierarchical time series.
method Bottom-Up Importance Sampling algorithm for any type of forecast distribution.
result Significant improvement over base probabilistic forecasts in experiments.
Boosted embeddings improve time series forecasting models.
problem Improving time series forecasting accuracy.
method Gradient boosting with DNN weak learners and incremental weight updates.
result DeepGB model outperforms state-of-the-art models.
Paper predicts future graph structures using time series methods.
problem Forecasting dynamic graph structures with unseen nodes and edges.
method Time series forecasting for node degree prediction combined with flux balance analysis.
result Demonstrated utility and applicability of the approach on synthetic and real-world datasets.
DeepAR improves probabilistic forecasting in retail and beyond.
problem Accurate probabilistic forecasting of time series data.
method Training an auto-regressive recurrent network on related time series.
result DeepAR achieves 15% accuracy improvement over state-of-the-art methods.
SPADE-S improves time series forecasting accuracy for low-magnitude and sparse data.
problem Challenges in forecasting time series with strong heterogeneity in magnitude and sparsity.
method SPADE-S is a robust forecasting architecture that reduces biases and improves overall prediction accuracy.
result SPADE-S outperforms existing state-of-the-art approaches across diverse use cases, improving forecast accuracy by up to 15%.
N-BEATS(P) efficiently forecasts millions of time series with reduced memory and time.
problem Efficiently forecasting millions of time series with high accuracy.
method Global parallel variant of N-BEATS model designed for multi-step time series forecasting.
result Significant reduction in training time and memory usage with comparable accuracy.
An ensemble of randomized NNs improves time series forecasting accuracy.
problem Forecasting time series with multiple seasonality and nonstationarity.
method Randomized neural networks with pattern-based time series representation and diversity control strategies.
result Outperforms statistical and machine learning models in forecasting accuracy.
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.
New deep probabilistic model handles missing data in time series forecasting.
problem Handling missing data in time series forecasting.
method Combination of deep learning and probabilistic methods.
result Advantage in forecasting and novelty detection with missing data.
ModelRadar evaluates forecasting models across multiple aspects.
problem Evaluating forecasting models using single scores hides relevant performance variations.
method ModelRadar, a framework for aspect-based evaluation of univariate time series forecasting models.
result NHITS performs best overall but its superiority varies with forecasting conditions.
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 uses evidence theory to improve stock price forecasting accuracy.
problem Inaccurate stock price predictions due to time series limitations.
method Applies evidence theory's confidence functions and Dempster combination rule to stock price forecasting.
result Improved accuracy in stock price predictions compared to classic methods.
Time series modeling and forecasting has fundamental importance to various practical domains. Thus a lot of active research works is going on in this subject during several years. Many important models have been proposed in literature for improving the accuracy and effectiveness of time series forecasting. The aim of t…
Simplifies forecast combination by using diversity of out-of-sample forecasts.
problem Estimating optimal weights for forecast combinations is challenging.
method Use out-of-sample forecasts to extract features and calculate weights for forecast combination.
result Achieves superior forecasting performance in point forecasts and prediction intervals.
Proceed adapts models proactively against concept drift in online time series forecasting.
problem Concept drift causes forecast models to adapt to outdated concepts, reducing performance.
method Proceed estimates and translates concept drift into parameter adjustments, enhancing model resilience.
result Proceed brings more performance improvements than state-of-the-art online learning methods.
Proposes QDF to improve multi-step time-series forecasting.
problem Ignoring label autocorrelation and unequal task weights in training objectives.
method Quadratic-form weighted training objective and QDF learning algorithm.
result Improves performance of various forecast models, achieving state-of-the-art results.