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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 5551–5600 of 6748 papers

TitleStatusHype
Black-box Bayesian inference for economic agent-based models—0
Blind Deinterleaving of Signals in Time Series with Self-attention Based Soft Min-cost Flow Learning—0
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes—0
Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference—0
BLUnet: Arithmetic-free Inference with Bit-serialised Table Lookup Operation for Efficient Deep Neural Networks—0
Boltzmann machines for time-series—0
Boosted Embeddings for Time Series Forecasting—0
Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting—0
Boosted p-Values for High-Dimensional Vector Autoregression—0
Boosting Information Extraction Systems with Character-level Neural Networks and Free Noisy Supervision—0
Boosting the kernelized shapelets: Theory and algorithms for local features—0
Bootstrap Inference for Hawkes and General Point Processes—0
Bootstrapping Non-Stationary Stochastic Volatility—0
bootUR: An R Package for Bootstrap Unit Root Tests—0
BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings—0
Brain dynamics via Cumulative Auto-Regressive Self-Attention—0
Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification—0
Brain Emotional Learning-Based Prediction Model (For Long-Term Chaotic Prediction Applications)—0
Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume—0
Brain-inspired photonic signal processor for periodic pattern generation and chaotic system emulation—0
BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning—0
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian—0
BRATI: Bidirectional Recurrent Attention for Time-Series Imputation—0
Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data—0
Bridging AIC and BIC: a new criterion for autoregression—0
Bridging factor and sparse models—0
Bridging observation, theory and numerical simulation of the ocean using Machine Learning—0
Bridging Subjective and Objective QoE: Operator-Level Aggregation Using LLM-Based Comment Analysis and Network MOS Comparison—0
Bridging the Gap: Decoding the Intrinsic Nature of Time in Market Data—0
BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly Detection in Multivariate Time Series—0
Building a Fuel Moisture Model for the Coupled Fire-Atmosphere Model WRF-SFIRE from Data: From Kalman Filters to Recurrent Neural Networks—0
Building a Multivariate Time Series Benchmarking Datasets Inspired by Natural Language Processing (NLP)—0
Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction—0
Building Deep Learning Models to Predict Mortality in ICU Patients—0
Building Floorspace in China: A Dataset and Learning Pipeline—0
Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis—0
Bursty and persistent properties of large-scale brain networks revealed with a point-based method for dynamic functional connectivity—0
bursty_dynamics: A Python Package for Exploring the Temporal Properties of Longitudinal Data—0
Business Cycle Synchronization in the EU: A Regional-Sectoral Look through Soft-Clustering and Wavelet Decomposition—0
"Butterfly Effect" vs Chaos in Energy Futures Markets—0
Byte Pair Encoding for Efficient Time Series Forecasting—0
Caformer: Rethinking Time Series Analysis from Causal Perspective—0
CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data—0
Calculation of Sub-bands 1,2,5,6 for 64-Point Complex FFT and Its extension to N (=2^N) Point FFT—0
Calibrating Agent-based Models to Microdata with Graph Neural Networks—0
Calibration and Filtering of Exponential L\'evy Option Pricing Models—0
Calibration-free B0 correction of EPI data using structured low rank matrix recovery—0
Calibration of Machine Learning Classifiers for Probability of Default Modelling—0
Calibration window selection based on change-point detection for forecasting electricity prices—0
Can Agent-Based Models Probe Market Microstructure?—0
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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