Time Series Analysis
1878 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 )
Benchmarks
These leaderboards are used to track progress in Time Series Analysis
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Libraries
Use these libraries to find Time Series Analysis models and implementationsDatasets
Speech Commands
PhysioNet Challenge 2012
EigenWorms
TSSB
Hurricane
Subtasks
Latest papers with no code
Time Series Analysis of Key Societal Events as Reflected in Complex Social Media Data Streams
Our approach is a novel mode to study multiple social media domains to distil key information which may be obscured otherwise, allowing for useful and actionable insights.
ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis
This paper introduces ConvTimeNet, a novel deep hierarchical fully convolutional network designed to serve as a general-purpose model for time series analysis.
Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks.
Time Series Analysis in Compressor-Based Machines: A Survey
In both industrial and residential contexts, compressor-based machines, such as refrigerators, HVAC systems, heat pumps and chillers, are essential to fulfil production and consumers' needs.
IMUOptimize: A Data-Driven Approach to Optimal IMU Placement for Human Pose Estimation with Transformer Architecture
This paper presents a novel approach for predicting human poses using IMU data, diverging from previous studies such as DIP-IMU, IMUPoser, and TransPose, which use up to 6 IMUs in conjunction with bidirectional RNNs.
Incorporating Taylor Series and Recursive Structure in Neural Networks for Time Series Prediction
Time series analysis is relevant in various disciplines such as physics, biology, chemistry, and finance.
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting
Time series analysis is vital for numerous applications, and transformers have become increasingly prominent in this domain.
Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
In this study, we present aLLM4TS, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning.
MOMENT: A Family of Open Time-series Foundation Models
Pre-training large models on time-series data is challenging due to (1) the absence of a large and cohesive public time-series repository, and (2) diverse time-series characteristics which make multi-dataset training onerous.
Multi-scale fMRI time series analysis for understanding neurodegeneration in MCI
The proposed approach is employed for classification of a cohort of 50 healthy control (HC) and 50 Mild Cognitive Impairment (MCI), sourced from ADNI dataset.
