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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.
The Power of Architecture: Deep Dive into Transformer Architectures for Long-Term Time Series Forecasting
Data Augmentation in Time Series Forecasting through Inverted Framework
MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models
Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching
Foundation models for time series forecasting: Application in conformal prediction
Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection
AI-Based Demand Forecasting and Load Balancing for Optimising Energy use in Healthcare Systems: A real case study
SEED: A Structural Encoder for Embedding-Driven Decoding in Time Series Prediction with LLMs
A foundation model with multi-variate parallel attention to generate neuronal activity
FAF: A Feature-Adaptive Framework for Few-Shot Time Series Forecasting
FlightKooba: A Fast Interpretable FTP Model
Scalable Machine Learning Algorithms using Path Signatures
Multi-Scale Finetuning for Encoder-based Time Series Foundation Models
SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting
Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation
Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworks
Forecast-Then-Optimize Deep Learning Methods
PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
MetaEformer: Unveiling and Leveraging Meta-patterns for Complex and Dynamic Systems Load Forecasting
A Review of the Long Horizon Forecasting Problem in Time Series Analysis
Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction
Diffusion-based Time Series Forecasting for Sewerage Systems
Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness
KARMA: A Multilevel Decomposition Hybrid Mamba Framework for Multivariate Long-Term Time Series Forecasting
Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated Data
MIRA: Medical Time Series Foundation Model for Real-World Health Data
Uncovering the Functional Roles of Nonlinearity in Memory
Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting
End-to-End Probabilistic Framework for Learning with Hard Constraints
A Statistical Framework for Model Selection in LSTM Networks
LETS Forecast: Learning Embedology for Time Series Forecasting
The cost of ensembling: is it always worth combining?
Trojan Horse Hunt in Time Series Forecasting for Space Operations
Stock Market Telepathy: Graph Neural Networks Predicting the Secret Conversations between MINT and G7 Countries
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