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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.
Towards Reliable Time Series Forecasting under Future Uncertainty: Ambiguity and Novelty Rejection Mechanisms
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems
Sentinel: Multi-Patch Transformer with Temporal and Channel Attention for Time Series Forecasting
Renewable Energy Transition in South America: Predictive Analysis of Generation Capacity by 2050
From S4 to Mamba: A Comprehensive Survey on Structured State Space Models
MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering
DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks
CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-series
Diffusion-Based Forecasting for Uncertainty-Aware Model Predictive Control
HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction
Theoretical Foundation of Flow-Based Time Series Generation: Provable Approximation, Generalization, and Efficiency
Epidemic Forecasting with a Hybrid Deep Learning Method Using CNN-LSTM With WOA-GWO Parameter Optimization: Global COVID-19 Case Study
Augmented Invertible Koopman Autoencoder for long-term time series forecasting
ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables
Hierarchical Information-Guided Spatio-Temporal Mamba for Stock Time Series Forecasting
Deep Learning for Time Series Forecasting: A Survey
Towards Efficient Large Scale Spatial-Temporal Time Series Forecasting via Improved Inverted Transformers
Mamba time series forecasting with uncertainty quantification
LLM-PS: Empowering Large Language Models for Time Series Forecasting with Temporal Patterns and Semantics
Minimal Time Series Transformer
LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization
MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting
Data Driven Decision Making with Time Series and Spatio-temporal Data
Enhancing Time Series Forecasting via Logic-Inspired Regularization
FinTSBridge: A New Evaluation Suite for Real-world Financial Prediction with Advanced Time Series Models
Fixing the Pitfalls of Probabilistic Time-Series Forecasting Evaluation by Kernel Quadrature
A Novel Distributed PV Power Forecasting Approach Based on Time-LLM
TimeFound: A Foundation Model for Time Series Forecasting
Hedging with Sparse Reward Reinforcement Learning
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMs
Graph-Augmented LSTM for Forecasting Sparse Anomalies in Graph-Structured Time Series
Lightweight Channel-wise Dynamic Fusion Model: Non-stationary Time Series Forecasting via Entropy Analysis
ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish
SeqFusion: Sequential Fusion of Pre-Trained Models for Zero-Shot Time-Series Forecasting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.