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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.
Zero-Shot Forecasting Mortality Rates: A Global Study
Stochastic Processes with Modified Lognormal Distribution Featuring Flexible Upper Tail
Context-Aware Probabilistic Modeling with LLM for Multimodal Time Series Forecasting
Logo-LLM: Local and Global Modeling with Large Language Models for Time Series Forecasting
Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline
Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion
Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning
IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting
Beyond Time: Cross-Dimensional Frequency Supervision for Time Series Forecasting
Nearest Neighbor Multivariate Time Series Forecasting
Avocado Price Prediction Using a Hybrid Deep Learning Model: TCN-MLP-Attention Architecture
ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Data
Does Scaling Law Apply in Time Series Forecasting?
Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting
FAS-LLM: Large Language Model-Based Channel Prediction for OTFS-Enabled Satellite-FAS Links
Feature Fitted Online Conformal Prediction for Deep Time Series Forecasting Model
A Multi-scale Representation Learning Framework for Long-Term Time Series Forecasting
SPAT: Sensitivity-based Multihead-attention Pruning on Time Series Forecasting Models
OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed Domain
Enhancing Time Series Forecasting via a Parallel Hybridization of ARIMA and Polynomial Classifiers
Non-Stationary Time Series Forecasting Based on Fourier Analysis and Cross Attention Mechanism
Matrix Is All You Need
Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering
FilterTS: Comprehensive Frequency Filtering for Multivariate Time Series Forecasting
Retrieval Augmented Time Series Forecasting
STRGCN: Capturing Asynchronous Spatio-Temporal Dependencies for Irregular Multivariate Time Series Forecasting
Non-stationary Diffusion For Probabilistic Time Series Forecasting
SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting
Less is More: Efficient Weight Farcasting with 1-Layer Neural Network
CASA: CNN Autoencoder-based Score Attention for Efficient Multivariate Long-term Time-series Forecasting
Learning the Simplest Neural ODE
Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation
Enhanced Prediction Model for Time Series Characterized by GARCH via Interval Type-2 Fuzzy Inference System
Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts
Feature Optimization for Time Series Forecasting via Novel Randomized Uphill Climbing
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