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Time Series Forecasting

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하위 태스크: 6
평가표: 86
보존된 평가 기록: 538

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>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/giacomolanciano/UCC2021-predictive-auto-scaling-openstack

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Predictive Auto-scaling with OpenStack Monasca

https://github.com/gitbooo/CrossViVit

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Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context

https://github.com/gonzalopezgil/xlstm-ts

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An Evaluation of Deep Learning Models for Stock Market Trend Prediction

https://github.com/google-research/google-research

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Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts

https://github.com/google-research/timesfm

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A decoder-only foundation model for time-series forecasting

https://github.com/google-research/zapbench

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ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish

https://github.com/google/vsf-time-series

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Multi-Variate Time Series Forecasting on Variable Subsets

https://github.com/grand-lab/mtgode

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Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

https://github.com/graph-and-geometric-learning/timeseriesccm

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From Similarity to Superiority: Channel Clustering for Time Series Forecasting

https://github.com/graph-machine-learning-group/taming-local-effects-stgnns

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Taming Local Effects in Graph-based Spatiotemporal Forecasting

https://github.com/grimmlab/evars-gpr

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EVARS-GPR: EVent-triggered Augmented Refitting of Gaussian Process Regression for Seasonal Data

https://github.com/half111/calibration_cds

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Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift

https://github.com/hank0626/calf

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CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning

https://github.com/hank0626/llata

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CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning

https://github.com/hank0626/timebridge

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TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting

https://github.com/hanlu-nju/channel_independent_mtsf

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The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting

https://github.com/hansk0812/Forecasting-Models

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A Review of the Long Horizon Forecasting Problem in Time Series Analysis

https://github.com/hanyuki23/SoP

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Non-collective Calibrating Strategy for Time Series Forecasting

https://github.com/haochenglouis/robusttsf

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RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies

https://github.com/haounsw/pisa

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PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting

https://github.com/hc-xai/dcits

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DCIts -- Deep Convolutional Interpreter for time series

https://github.com/hdtkk/HDT

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HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting

https://github.com/helmholtz-ai-energy/recycle

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ReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers

https://github.com/hharcolezi/ldp-protocols-mobility-cdrs/tree/main/papers/%5B3%5D

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Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?

https://github.com/hitshenrj/fi2vts

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Fi$^2$VTS: Time Series Forecasting Via Capturing Intra- and Inter-Variable Variations in the Frequency Domain

https://github.com/houjingyi-ustb/discover_plf

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Discovering Predictable Latent Factors for Time Series Forecasting

https://github.com/houyikai/ldm-logsparse-decomposable-multiscaling

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Breaking the Context Bottleneck on Long Time Series Forecasting

https://github.com/howard-hou/rwkv-ts

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RWKV-TS: Beyond Traditional Recurrent Neural Network for Time Series Tasks

https://github.com/huangst21/timekan

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TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting

https://github.com/huaxiuyao/DMVST-Net

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Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

https://github.com/huihevv/fairfor

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Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective

https://github.com/humaticslab/gtm-transformer

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Well Googled is Half Done: Multimodal Forecasting of New Fashion Product Sales with Image-based Google Trends

https://github.com/humaticslab/pop-mining-potential-performance

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POP: Mining POtential Performance of new fashion products via webly cross-modal query expansion

https://github.com/hundredl/mg-tsd

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MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process

https://github.com/hysonlab/graphlasso

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Sparsity exploitation via discovering graphical models in multi-variate time-series forecasting

https://github.com/hyunwookl/tilde-q

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TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting

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