SOTAVerified

Time Series Analysis

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 )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6076–6100 of 6748 papers

TitleStatusHype
Stochastic Gradient MCMC for State Space ModelsCode0
The Wasserstein-Fourier Distance for Stationary Time SeriesCode0
Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial NetworksCode0
An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different DimensionsCode0
Warped Dynamic Linear Models for Time Series of CountsCode0
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series ForecastingCode0
Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite ImageryCode0
On Transfer Learning For Chatter Detection in Turning Using Wavelet Packet Transform and Empirical Mode DecompositionCode0
Detecting and Explaining Causes From Text For a Time Series EventCode0
Learning Physical Concepts in Cyber-Physical Systems: A Case StudyCode0
Open-domain Event Extraction and Embedding for Natural Gas Market PredictionCode0
Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast TasksCode0
Variational Encoding of Complex DynamicsCode0
Opinion Prediction with User FingerprintingCode0
Learning Procedural Abstractions and Evaluating Discrete Latent Temporal StructureCode0
Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample DatasetsCode0
Optimal activity and battery scheduling algorithm using load and solar generation forecastCode0
Stochastic Weight Matrix-based Regularization Methods for Deep Neural NetworksCode0
Robust Parameter-Free Season Length Detection in Time SeriesCode0
StockBot: Using LSTMs to Predict Stock PricesCode0
Learning Representations for Time Series ClusteringCode0
Learning Representations from EEG with Deep Recurrent-Convolutional Neural NetworksCode0
CORAD: Correlation-Aware Compression of Massive Time Series using Sparse Dictionary CodingCode0
Derivative Delay Embedding: Online Modeling of Streaming Time SeriesCode0
COPER: Continuous Patient State PerceiverCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified