Time Series Prediction
111 papers with code • 2 benchmarks • 11 datasets
The goal of Time Series Prediction is to infer the future values of a time series from the past.
Source: Orthogonal Echo State Networks and stochastic evaluations of likelihoods
Libraries
Use these libraries to find Time Series Prediction models and implementationsDatasets
EarthNet2021
ExtMarker
Air Quality Index
Data Collected with Package Delivery Quadcopter Drone
Hotel Sales
Hurricane
Latest papers
A Neuro-Symbolic Approach for Enhanced Human Motion Prediction
sariahmghames/neurosym-prediction
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Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e. g. robots).
Multi-task Meta Label Correction for Time Series Prediction
senyuanya/mmlc
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To address the above issues, we create a label correction method to time series data with meta-learning under a multi-task framework.
Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction
The prediction results of deep learning algorithms are compared with default hyperparameters and random search algorithms to confirm the efficacy of the genetic algorithm approach.
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
However, GNN-based models have three major limitations for traffic prediction: i) Most methods model spatial dependencies in a static manner, which limits the ability to learn dynamic urban traffic patterns; ii) Most methods only consider short-range spatial information and are unable to capture long-range spatial dependencies; iii) These methods ignore the fact that the propagation of traffic conditions between locations has a time delay in traffic systems.
Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode Decomposition
In the prediction and reconstruction phase, each of the IMFs is given to a separate MFRFNN for prediction, and predicted signals are summed to reconstruct the output.
Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting
duongtrung/time-series-temporal-saliency-patterns
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Despite the notable advancements in numerous Transformer-based models, the task of long multi-horizon time series forecasting remains a persistent challenge, especially towards explainability.
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks
Forecasting time series with extreme events has been a challenging and prevalent research topic, especially when the time series data are affected by complicated uncertain factors, such as is the case in hydrologic prediction.
HigeNet: A Highly Efficient Modeling for Long Sequence Time Series Prediction in AIOps
Modern IT system operation demands the integration of system software and hardware metrics.
Time Series Prediction for Food sustainability
With exponential growth in the human population, it is vital to conserve natural resources without compromising on producing enough food to feed everyone.
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables
While exogenous variables have a major impact on performance improvement in time series analysis, inter-series correlation and time dependence among them are rarely considered in the present continuous methods.
