# Time-Series Few-Shot Learning with Heterogeneous Channels

5 papers with code • 1 benchmarks • 1 datasets

## Most implemented papers

# Generative Adversarial Networks

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.

# N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

We focus on solving the univariate times series point forecasting problem using deep learning.

# Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

We propose a simple but strong baseline for time series classification from scratch with deep neural networks.

# Meta-learning from Tasks with Heterogeneous Attribute Spaces

We propose a heterogeneous meta-learning method that trains a model on tasks with various attribute spaces, such that it can solve unseen tasks whose attribute spaces are different from the training tasks given a few labeled instances.

# Few-Shot Forecasting of Time-Series with Heterogeneous Channels

Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set.