Univariate Time Series Forecasting
20 papers with code • 3 benchmarks • 6 datasets
Benchmarks
These leaderboards are used to track progress in Univariate Time Series Forecasting
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Libraries
Use these libraries to find Univariate Time Series Forecasting models and implementations
ETT
Electricity
ExtMarker
Hurricane
Most implemented papers
Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms
awslabs/gluonts
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This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.
W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting
Deep learning utilizing transformers has recently achieved a lot of success in many vital areas such as natural language processing, computer vision, anomaly detection, and recommendation systems, among many others.
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.
LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
ai4cts/lightcts
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Many deep learning models have been proposed to improve the accuracy of CTS forecasting.
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting
john-x-jiang/meta_ssm
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ICLR 2023
We compared the presented framework with a comprehensive set of baseline models trained 1) globally on the large meta-training set with diverse dynamics, and 2) individually on single dynamics, both with and without fine-tuning to k-shot support series used by the meta-models.
Feature-aligned N-BEATS with Sinkhorn divergence
We propose Feature-aligned N-BEATS as a domain-generalized time series forecasting model.
A New Deep Learning Architecture withInductive Bias Balance for Transformer Oil Temperature Forecasting
manjimnav/SRCNet
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Journal of Big Data 2023
In this work, we develop a new deep learning architecture that obtain an efficacy which compete with the best current architectures in transformer oil temperature forecasting while improve the efficacy.
PHILNet: A Novel Efficient Approach for Time Series Forecasting using Deep Learning
manjimnav/PHILNet
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Information Sciences 2023
Time series is one of the most common data types in the industry nowadays.
Multi-horizon short-term load forecasting using hybrid of LSTM and modified split convolution
The concatenating order of LSTM and SC in the proposed hybrid network provides an excellent capability of extraction of sequence-dependent features and other hierarchical spatial features.
TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Next, we employ TFB to perform a thorough evaluation of 21 Univariate Time Series Forecasting (UTSF) methods on 8, 068 univariate time series and 14 Multivariate Time Series Forecasting (MTSF) methods on 25 datasets.
