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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.
Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting
Wisdom of the Crowds in Forecasting: Forecast Summarization for Supporting Future Event Prediction
HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting
A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting
Two-stage hybrid models for enhancing forecasting accuracy on heterogeneous time series
Linear Transformers as VAR Models: Aligning Autoregressive Attention Mechanisms with Autoregressive Forecasting
Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time Series Forecasting Based on Biological ODEs
Exploring Patterns Behind Sports
Powerformer: A Transformer with Weighted Causal Attention for Time-series Forecasting
TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting
Enhancing Financial Time-Series Forecasting with Retrieval-Augmented Large Language Models
The $α$-Alternator: Dynamic Adaptation To Varying Noise Levels In Sequences Using The Vendi Score For Improved Robustness and Performance
Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting
CAPE: Covariate-Adjusted Pre-Training for Epidemic Time Series Forecasting
Benchmarking Time Series Forecasting Models: From Statistical Techniques to Foundation Models in Real-World Applications
Rule-based Evolving Fuzzy System for Time Series Forecasting: New Perspectives Based on Type-2 Fuzzy Sets Measures Approach
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
Forecasting VIX using interpretable Kolmogorov-Arnold networks
Binned Spectral Power Loss for Improved Prediction of Chaotic Systems
Examining the Impact of Income Inequality and Gender on School Completion in Malaysia: A Machine Learning Approach Utilizing Malaysia's Public Sector Open Data
BEAT: Balanced Frequency Adaptive Tuning for Long-Term Time-Series Forecasting
Long-term prediction of El Niño-Southern Oscillation using reservoir computing with data-driven realtime filter
Transformer Based Time-Series Forecasting for Stock
Toward Relative Positional Encoding in Spiking Transformers
Data-Driven vs Traditional Approaches to Power Transformer's Top-Oil Temperature Estimation
Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecasting
SWIFT: Mapping Sub-series with Wavelet Decomposition Improves Time Series Forecasting
Deterministic Reservoir Computing for Chaotic Time Series Prediction
FreqMoE: Enhancing Time Series Forecasting through Frequency Decomposition Mixture of Experts
VarDrop: Enhancing Training Efficiency by Reducing Variate Redundancy in Periodic Time Series Forecasting
GenTL: A General Transfer Learning Model for Building Thermal Dynamics
FreEformer: Frequency Enhanced Transformer for Multivariate Time Series Forecasting
Multimodal Stock Price Prediction
TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting
Automatic selection of the best neural architecture for time series forecasting via multi-objective optimization and Pareto optimality conditions
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