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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 Time Series Forecasting Using Kolmogorov Arnold Networks
QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning
A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting
TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysis
Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecasting
EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictions
Learning Latent Spaces for Domain Generalization in Time Series Forecasting
DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
Financial Fine-tuning a Large Time Series Model
Quantum Kernel-Based Long Short-term Memory for Climate Time-Series Forecasting
Auto-Regressive Moving Diffusion Models for Time Series Forecasting
Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model
Adversarial Vulnerabilities in Large Language Models for Time Series Forecasting
Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting
LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care
Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond
Rethinking Time Series Forecasting with LLMs via Nearest Neighbor Contrastive Learning
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization
KEDformer:Knowledge Extraction Seasonal Trend Decomposition for Long-term Sequence Prediction
WinTSR: A Windowed Temporal Saliency Rescaling Method for Interpreting Time Series Deep Learning Models
Higher Order Transformers: Efficient Attention Mechanism for Tensor Structured Data
LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data
How Much Can Time-related Features Enhance Time Series Forecasting?
FSMLP: Modelling Channel Dependencies With Simplex Theory Based Multi-Layer Perceptions In Frequency Domain
DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series Analysis
Fine-Tuning Pre-trained Large Time Series Models for Prediction of Wind Turbine SCADA Data
Signal Processing over Time-Varying Graphs: A Systematic Review
An Adversarial Learning Approach to Irregular Time-Series Forecasting
Conformalised Conditional Normalising Flows for Joint Prediction Regions in time series
Disentangled Interpretable Representation for Efficient Long-term Time Series Forecasting
MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting
Time-Series Forecasting in Smart Manufacturing Systems: An Experimental Evaluation of the State-of-the-art Algorithms
MTS-UNMixers: Multivariate Time Series Forecasting via Channel-Time Dual Unmixing
Enhancing Project Performance Forecasting using Machine Learning Techniques
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