Proposes a method for forecasting time series with multiple seasonality.
problem Forecasting time series with both short-term and long-term seasonality is challenging.
method Two-stage method: first generalizes ARMA model for multiple seasonality, second selects lag order.
result Method outperforms `Facebook Prophet` model in predictive performance.
Modeling regime shifts in co-evolving time series with interactions and time-dependency.
problem Discovering and modeling regime shifts in multiple time series with relationships and time-dependent behaviors.
method Modeling interactions and time-dependency in co-evolving time series using a mapping grid and dynamic network representation for regime identification and time-dependent Cox regression for regime transition probabilities.
result A principled approach for modeling interactions and time-dependency in co-evolving time series.
TimeMCL forecasts diverse time series futures using neural networks and WTA loss.
problem Forecasting multiple plausible time series futures.
method Multiple Choice Learning (MCL) framework with Winner-Takes-All (WTA) loss.
result TimeMCL efficiently predicts diverse time series futures at low computational cost.
GANF uses normalizing flows to detect anomalies in multiple time series.
problem Detecting anomalies in multiple time series with interdependencies.
method Bayesian network integration with normalizing flows for unsupervised anomaly detection.
result GANF effectively detects anomalies and identifies distribution drift in time series data.
A new test for volatility in clustered time series data, robust to distributional assumptions.
problem Volatility issues in clustered multiple time series data, especially in stock market indicators.
method Bootstrap method for multiple time series, accounting for contagion effect.
result The test is correctly sized and powerful, especially for stationary mean and contained volatility in fewer clusters.
Geometric pruning rules improve change point detection in multiple time series.
problem Detecting multiple changes in multiple independent time series.
method Dynamic programming algorithms with inequality-based and geometric pruning rules.
result Geometric pruning rules offer close-to-linear time complexity for multiple independent time series.
Proposes BHT-ARIMA for forecasting multiple short time series.
problem Forecasting multiple short time series with mutual correlations.
method Block Hankel tensors, Tucker decomposition, generalized tensor ARIMA.
result Improves forecasting accuracy and reduces computational cost.
NTW aligns multiple time-series data efficiently using neural networks.
problem Multiple sequence alignment for time-series analyses.
method Neural time warping that relaxes the MSA to a continuous optimization problem.
result NTW successfully aligns a hundred time-series and outperforms existing methods.
Paper introduces SMM for forecasting multiple time series with missing values.
problem Forecasting multiple time series with missing and noisy values.
method Sliding Mask Method (SMM) using Non-negative Matrix Factorization (NMF).
result The method outperforms state-of-the-art methods in time series forecasting.
Method selects the best deep learner for time-series prediction using Bayesian networks.
problem Selecting the most effective deep learning model for time-series prediction.
method Bayesian network selects deep learners based on input variables and cluster training data.
result Threshold value determines which deep learners predict time-series data robustly.
Paper proposes a robust framework for detecting multiple periodic components in time series.
problem Detecting multiple periodic components in time series with interlaced patterns and external noise.
method Applying maximal overlap discrete wavelet transform to isolate periodic components, ranking them by wavelet variance, and detecting single periodicity robustly.
result The proposed algorithm outperforms other methods for both single and multiple periodicity detection.
ST-MTM models complex time series by decomposing and masking seasonal and trend components.
problem Forecasting complex time series with intricate temporal variations.
method Seasonal-Trend Decomposition with Masking and Contrastive Learning.
result ST-MTM achieves superior forecasting performance compared to existing methods.
In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we al…
A novel non-supervised method detects anomalies in multivariate time series.
problem Detecting anomalies in multivariate time series data.
method Partitioning based on clustering of correlation coefficients.
result Significant improvement in anomaly detection performance.
Analyzing multivariate time series data is important to predict future events and changes of complex systems in finance, manufacturing, and administrative decisions. The expressiveness power of Gaussian Process (GP) regression methods has been significantly improved by compositional covariance structures. In this paper…
Optimal model selection for forecasting large collections of short time series using latent space.
problem Challenges in choosing among multiple forecasting methods for large, high-dimensional time series with limited data.
method Combining low-rank temporal matrix factorization with optimal model selection using cross-validation.
result Forecasting latent factors leads to significant performance gains compared to direct uni-variate model application.
Proposes a test for shared information between time series and events.
problem Detecting extreme events in time series data.
method Non-parametric statistical test using multiple two-sample testing at increasing lags.
result Outperforms or matches related tests on various datasets.
Model financial time series with MOGP for imputation and prediction.
problem Impute missing financial data due to dependencies among multiple series.
method Use a multi-output Gaussian process (MOGP) with expressive covariance functions.
result The model outperforms other MOGPs and independent Gaussian process on real financial data.
Generating forecasts for time series with multiple seasonal cycles is an important use-case for many industries nowadays. Accounting for the multi-seasonal patterns becomes necessary to generate more accurate and meaningful forecasts in these contexts. In this paper, we propose Long Short-Term Memory Multi-Seasonal Net…
ReGENN improves time series forecasting by considering inter and intra-temporal relationships.
problem Achieving reliable predictions in real-world time series applications.
method ReGENN combines graph evolution with deep recurrent learning to model dynamic dependencies among multiple variables.
result Sound improvement of up to 64.87% over competing algorithms in time-series forecasting.
The project aims to research on combining deep learning specifically Long-Short Memory (LSTM) and basic statistics in multiple multistep time series prediction. LSTM can dive into all the pages and learn the general trends of variation in a large scope, while the well selected medians for each page can keep the special…
An ensemble of randomized NNs improves time series forecasting accuracy.
problem Forecasting time series with multiple seasonality and nonstationarity.
method Randomized neural networks with pattern-based time series representation and diversity control strategies.
result Outperforms statistical and machine learning models in forecasting accuracy.
A technique uncovers latent causal relationships in multiple time series data.
problem Identifying causal relationships in complex, dynamic systems.
method Blindly identifies latent sources by projecting observed data into pairs of components to maximize causality.
result Reveals multiple strong causal relationships not evident in observed data.
theft package simplifies feature extraction for time series analysis in R.
problem Lack of a unified access point and methodological pipelines for feature-based time series analysis.
method theft package provides a unified framework for computing features from six open-source time series feature sets.
result theft enables comprehensive quantification and interpretation of time series structure.
Model predicts future values and fills in missing data.
problem Missing data in time series data.
method End-to-end time series model with two neural networks.
result Framework performs well in imputation and forecasting.
A hybrid loss framework improves time series forecasting by balancing global and component errors.
problem Current time series methods may prioritize less significant sub-series, leading to forecasting bias.
method Proposes a hybrid loss framework combining global and component losses, dynamically adjusting weights.
result Improves time series forecasting performance by 0.5-2% on average.
Accurate time series prediction over long future horizons is challenging and of great interest to both practitioners and academics. As a well-known intelligent algorithm, the standard formulation of Support Vector Regression (SVR) could be taken for multi-step-ahead time series prediction, only relying either on iterat…
This work proposes a method to learn graph structure for multivariate time series forecasting.
problem Improving multivariate time series forecasting by leveraging pairwise information.
method Learning a probabilistic graph model through optimizing mean performance over graph distribution parameterized by a neural network.
result Our method outperforms existing approaches in simplicity, efficiency, and performance.
HopCPT improves conformal prediction for time series with temporal dependencies.
problem Uncertainty quantification in time series data.
method HopCPT, a novel conformal prediction approach for time series that leverages temporal dependencies.
result HopCPT outperforms state-of-the-art methods on multiple real-world time series datasets.
Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix entries. The objective is to estimate multiple time series at a fine temporal scale from temporal aggregates measured on each individual serie…
Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the …
Study compares forecasting methods for logistics time series.
problem Improving forecasting accuracy in logistics.
method Compared statistical and machine learning methods on simulated time series.
result Statistical methods outperformed machine learning in one-step forecasts.
We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups. Our motivation comes from neuroscience, where an important problem is to identify, within a large assembly of neurons, subsets that respond similarly to a stimulu…
Fisher et al. extend multi-VAR for better modeling of heterogeneous time series.
problem Modeling structurally heterogeneous processes in social, health, and behavioral sciences.
method Adaptive weighting schemes for penalized estimation of multiple-subject multivariate time series.
result Improved estimation performance compared to alternative estimators.
This paper advocates Riemannian multi-manifold modeling in the context of network-wide non-stationary time-series analysis. Time-series data, collected sequentially over time and across a network, yield features which are viewed as points in or close to a union of multiple submanifolds of a Riemannian manifold, and dis…
The paper develops a method to forecast financial risk multiple steps ahead using quantile time series and historical simulation.
problem Forecasting financial risk multiple steps ahead with accurate estimation of Value-at-Risk (VaR) and Expected Shortfall (ES).
method Quantile-based, semi-parametric historical simulation estimation of VaR and ES models, using quantile loss function and resampling.
result The proposed method accurately forecasts VaR and ES one and multiple steps ahead, superior to existing methods.
New model predicts multiple future trends from merchant transactions.
problem Predicting multiple future trends from merchant transaction history.
method Convolutional neural networks and encoder-decoder structure.
result Demonstrated effectiveness in predicting multiple future trends.
We present techniques for effective Gaussian process (GP) modelling of multiple short time series. These problems are common when applying GP models independently to each gene in a gene expression time series data set. Such sets typically contain very few time points. Naive application of common GP modelling techniques…
MultiRocket boosts TSC speed and accuracy with pooling and transformations.
problem Efficient time series classification with high accuracy.
method Multiple pooling operators and transformations applied to raw and differenced series.
result MultiRocket outperforms MiniRocket and is competitive with state-of-the-art methods in terms of accuracy and speed.
grangersearch tests causal relationships in time series data.
problem Testing causal relationships between multiple time series.
method Exhaustive pairwise search, automatic lag order optimization, tidyverse integration.
result Automated Granger causality testing simplifies causal analysis.
N-BEATS(P) efficiently forecasts millions of time series with reduced memory and time.
problem Efficiently forecasting millions of time series with high accuracy.
method Global parallel variant of N-BEATS model designed for multi-step time series forecasting.
result Significant reduction in training time and memory usage with comparable accuracy.
Few-shot learning improves time-series forecasting with limited data.
problem Limited data in target tasks degrade forecasting performance.
method A few-shot learning method using recurrent neural networks with attention.
result The model forecasts future values effectively with minimal data.
Despite the eminent successes of deep neural networks, many architectures are often hard to transfer to irregularly-sampled and asynchronous time series that commonly occur in real-world datasets, especially in healthcare applications. This paper proposes a novel approach for classifying irregularly-sampled time series…
ModelRadar evaluates forecasting models across multiple aspects.
problem Evaluating forecasting models using single scores hides relevant performance variations.
method ModelRadar, a framework for aspect-based evaluation of univariate time series forecasting models.
result NHITS performs best overall but its superiority varies with forecasting conditions.
Translating potential disease biomarkers between multi-species 'omics' experiments is a new direction in biomedical research. The existing methods are limited to simple experimental setups such as basic healthy-diseased comparisons. Most of these methods also require an a priori matching of the variables (e.g., genes o…
Quantum model generates financial data with fewer parameters.
problem Generating financial data with fewer parameters.
method Applied time-series quantum generative model to financial data.
result Fewer parameters required compared to classical methods.
A new SOHP filter improves trend estimation in economic time series.
problem Improving trend estimation in nonlinear economic time series.
method Recursive application of one-sided HP filter on updated cyclical components, combined with an incremental HP filtering algorithm.
result Better performance of SOHP filter compared to other HP-type filters on real economic data.
Time series anomaly detection plays a critical role in automated monitoring systems. Most previous deep learning efforts related to time series anomaly detection were based on recurrent neural networks (RNN). In this paper, we propose a time series segmentation approach based on convolutional neural networks (CNN) for …