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Time Series Forecasting
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Time Series Forecasting is the task of fitting a model to historical, time-stamped data in order to predict future values. Traditional approaches include moving average, exponential smoothing, and ARIMA, though models as various as RNNs, Transformers, or XGBoost can also be applied. The most popular benchmark is the ETTh1 dataset. Models are typically evaluated using the Mean Square Error (MSE) or Root Mean Square Error (RMSE).
<span style="color:grey; opacity: 0.6">( Image credit: ThaiBinh Nguyen )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Patch-wise Structural Loss for Time Series Forecasting
Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models
A Compact Model for Large-Scale Time Series Forecasting
Efficient Time Series Forecasting via Hyper-Complex Models and Frequency Aggregation
PFformer: A Position-Free Transformer Variant for Extreme-Adaptive Multivariate Time Series Forecasting
Evaluating System 1 vs. 2 Reasoning Approaches for Zero-Shot Time Series Forecasting: A Benchmark and Insights
FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting
A Deep-Unfolding Approach to RIS Phase Shift Optimization Via Transformer-Based Channel Prediction
TSKANMixer: Kolmogorov-Arnold Networks with MLP-Mixer Model for Time Series Forecasting
ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series Forecasting
Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators
Predicting Bad Goods Risk Scores with ARIMA Time Series: A Novel Risk Assessment Approach
TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data
A Supervised Screening and Regularized Factor-Based Method for Time Series Forecasting
Beyond Fixed Variables: Expanding-variate Time Series Forecasting via Flat Scheme and Spatio-temporal Focal Learning
Explaining the Success of Nearest Neighbor Methods in Prediction
CoRe: Coherency Regularization for Hierarchical Time Series
Spatiotemporal Forecasting in Climate Data Using EOFs and Machine Learning Models: A Case Study in Chile
Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting
TimeDistill: Efficient Long-Term Time Series Forecasting with MLP via Cross-Architecture Distillation
Learning Novel Transformer Architecture for Time-series Forecasting
Position: There are no Champions in Long-Term Time Series Forecasting
Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting
Lightweight Online Adaption for Time Series Foundation Model Forecasts
S2TX: Cross-Attention Multi-Scale State-Space Transformer for Time Series Forecasting
IMTS-Mixer: Mixer-Networks for Irregular Multivariate Time Series Forecasting
On Creating a Causally Grounded Usable Rating Method for Assessing the Robustness of Foundation Models Supporting Time Series
Vision-Enhanced Time Series Forecasting via Latent Diffusion Models
A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective
AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting
Forecasting time series with constraints
HADL Framework for Noise Resilient Long-Term Time Series Forecasting
Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative
Quantifying Cryptocurrency Unpredictability: A Comprehensive Study of Complexity and Forecasting
Relational Conformal Prediction for Correlated Time Series
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