# Time Series Analysis

1867 papers with code • 3 benchmarks • 20 datasets

**Time Series Analysis** is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

## Libraries

Use these libraries to find Time Series Analysis models and implementations## Datasets

## Subtasks

## Most implemented papers

# Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting

Multi-horizon forecasting problems often contain a complex mix of inputs -- including static (i. e. time-invariant) covariates, known future inputs, and other exogenous time series that are only observed historically -- without any prior information on how they interact with the target.

# An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory.

# Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.

# Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.

# DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

Probabilistic forecasting, i. e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes.

# 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.

# Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain.

# A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades.

# Latent ODEs for Irregularly-Sampled Time Series

Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs).

# Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data.