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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

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Showing 3076–3100 of 6748 papers

TitleStatusHype
Fast and Robust Online Inference with Stochastic Gradient Descent via Random ScalingCode0
Distributed Learning and its Application for Time-Series Prediction—0
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition—0
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator—0
Causal Graph Discovery from Self and Mutually Exciting Time Series—0
Latent Time-Adaptive Drift-Diffusion Model—0
A General Method for Event Detection on Social Media—0
Homological Time Series Analysis of Sensor Signals from Power PlantsCode0
MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled TrajectoriesCode0
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems—0
The relationship between the US broad money supply and US GDP for the time period 2001 to 2019 with that of the corresponding time series for US national property and stock market indices, using an information entropy methodology—0
Causal Digital Twin from Multi-channel IoT—0
Warming up recurrent neural networks to maximise reachable multistability greatly improves learning—0
Deep Signature Statistics for Likelihood-free Time-series Models—0
Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural NetworksCode0
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes—0
Quantifying the Effects of COVID-19 on Restaurant Reviews—0
Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments—0
An improved LogNNet classifier for IoT application—0
Pattern Discovery in Time Series with Byte Pair Encoding—0
Rethinking the constraints of multimodal fusion: case study in Weakly-Supervised Audio-Visual Video Parsing—0
Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data—0
CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline SummarizationCode0
Asset volatility forecasting:The optimal decay parameter in the EWMA model—0
The Power of Log-Sum-Exp: Sequential Density Ratio Matrix Estimation for Speed-Accuracy OptimizationCode0
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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