Time Series Prediction
110 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 with no code
Signal-noise separation using unsupervised reservoir computing
We use Reservoir Computing (RC) to extract the maximum portion of "predictable information" from a given signal.
Incorporating Domain Differential Equations into Graph Convolutional Networks to Lower Generalization Discrepancy
We theoretically derive conditions where GCNs incorporating such domain differential equations are robust to mismatched training and testing data compared to baseline domain agnostic models.
Feature-Based Echo-State Networks: A Step Towards Interpretability and Minimalism in Reservoir Computer
This paper proposes a novel and interpretable recurrent neural-network structure using the echo-state network (ESN) paradigm for time-series prediction.
QEAN: Quaternion-Enhanced Attention Network for Visual Dance Generation
The study of music-generated dance is a novel and challenging Image generation task.
Analysis and Fully Memristor-based Reservoir Computing for Temporal Data Classification
Reservoir computing (RC) offers a neuromorphic framework that is particularly effective for processing spatiotemporal signals.
A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting
Time series forecasting is one of the most essential and ubiquitous tasks in many business problems, including demand forecasting and logistics optimization.
Learning to Program Variational Quantum Circuits with Fast Weights
This paper introduces the Quantum Fast Weight Programmers (QFWP) as a solution to the temporal or sequential learning challenge.
Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting
By simulating data, we can compare our forecast distribution over time against a full simulation of the actual distribution of our test set, thereby reducing the inherent uncertainty in testing time series models on real data.
Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities
However, there is a research gap in the LLMs' preferences in this field.
Unconventional Computing based on Four Wave Mixing in Highly Nonlinear Waveguides
In this work we numerically analyze a photonic unconventional accelerator based on the four-wave mixing effect in highly nonlinear waveguides.
