arXiv research

Improved prediction of hierarchical time series using structured regularization.

Proposes a neural network for accurate and reconciled hierarchical time series forecasting.

Paper proposes a new method for selecting the best hierarchical forecasting approach.

End-to-end deep model for coherent probabilistic forecasts in hierarchical time series.

A new hierarchical forecasting method improves overall accuracy.

CoRe method improves time series forecasting coherency without strict constraints.

Enhances neural forecasting for hierarchically organized time series data.

Proposes an evolutionary approach to fitting acyclic VAR models.

Paper proposes a hierarchical approach for early anomaly detection in time series data for critical health events.

Time series prediction has been studied in a variety of domains. However, it is still challenging to predict future series given historical observations and past exogenous data. Existing methods either fail to consider the interactions among different components of exogenous variables which may affect the prediction ac…

When forecasting time series with a hierarchical structure, the existing state of the art is to forecast each time series independently, and, in a post-treatment step, to reconcile the time series in a way that respects the hierarchy (Hyndman et al., 2011; Wickramasuriya et al., 2018). We propose a new loss function th…

New RDPC dissimilarity measure improves time series clustering.

Bayesian stacking improves model performance with varying model weights.

Time series of counts arise in a variety of forecasting applications, for which traditional models are generally inappropriate. This paper introduces a hierarchical Bayesian formulation applicable to count time series that can easily account for explanatory variables and share statistical strength across groups of rela…

Proposes a new algorithm for efficient probabilistic reconciliation of forecasts.

Hierarchical forecasting (HF) is needed in many situations in the supply chain (SC) because managers often need different levels of forecasts at different levels of SC to make a decision. Top-Down (TD), Bottom-Up (BU) and Optimal Combination (COM) are common HF models. These approaches are static and often ignore the d…

Hierarchical hidden Markov models predict market trends in financial time series.

A new multi-phase approach improves supply chain forecasting accuracy.

We first pursue the study of how hierarchy provides a well-adapted tool for the analysis of change. Then, using a time sequence-constrained hierarchical clustering, we develop the practical aspects of a new approach to wavelet regression. This provides a new way to link hierarchical relationships in a multivariate time…

The paper proposes blending gradient boosted trees and neural networks for hierarchical time series forecasting.

Study compares local and global models for hierarchical forecasting accuracy.

While we are usually focused on forecasting future values of time series, it is often valuable to additionally predict their entire probability distributions, e.g. to evaluate risk, Monte Carlo simulations. On example of time series of $\approx$ 30000 Dow Jones Industrial Averages, there will be presented application o…

We report evidence of a deep interplay between cross-correlations hierarchical properties and multifractality of New York Stock Exchange daily stock returns. The degree of multifractality displayed by different stocks is found to be positively correlated to their depth in the hierarchy of cross-correlations. We propose…

This paper proposes a hierarchical feature extractor for non-stationary streaming time series based on the concept of switching observable Markov chain models. The slow time-scale non-stationary behaviors are considered to be a mixture of quasi-stationary fast time-scale segments that are exhibited by complex dynamical…

Bayesian framework uses context trees for efficient time series modeling.

New method uses Cantor embeddings and Wasserstein distances to analyze predictive states in time series data.

In order to improve the efficiency and sustainability of electricity systems, most countries worldwide are deploying advanced metering infrastructures, and in particular household smart meters, in the residential sector. This technology is able to record electricity load time series at a very high frequency rates, info…

Model learns collective and individual dynamics in time series data.

Hierarchical organization is a cornerstone of complexity and multifractality constitutes its central quantifying concept. For model uniform cascades the corresponding singularity spectra are symmetric while those extracted from empirical data are often asymmetric. Using the selected time series representing such divers…

Bayesian analysis of financial time series using R-INLA.

An empirical analysis of interest rates in money and capital markets is performed. We investigate a set of 34 different weekly interest rate time series during a time period of 16 years between 1982 and 1997. Our study is focused on the collective behavior of the stochastic fluctuations of these time-series which is in…

Unified framework for integrating linear constraints in time series forecasting.

Benchmark study evaluates 8 clustering methods on 99 UCR time series datasets.

Proposes a new approach to time series representation learning by embedding patches independently.

Multiplicative random cascade model naturally reproduces the intermittency or multifractality, which is frequently shown among hierarchical complex systems such as turbulence and financial markets. As described herein, we investigate the validity of a multiplicative hierarchical random cascade model through an empirica…

This paper proposes a nonparametric Bayesian method for exploratory data analysis and feature construction in continuous time series. Our method focuses on understanding shared features in a set of time series that exhibit significant individual variability. Our method builds on the framework of latent Diricihlet alloc…

GBOC detects anomalies in time series data using granular-ball vectors.

Method summarizes and predicts time series data for COVID-19 cases and deaths.

A nonparametric method for time series analysis extracts envelopes, detects peaks, and clusters data.

Bayesian method clusters time series with varying dynamics.

We present a novel blind source separation (BSS) method, called information geometric blind source separation (IGBSS). Our formulation is based on the log-linear model equipped with a hierarchically structured sample space, which has theoretical guarantees to uniquely recover a set of source signals by minimizing the K…

Using data from a sample of 28 representatives countries, we propose a classification of currency crises consequences based on the ultrametric analysis of the real exchange rate movements time series, without any further assumption. By using the matrix of synchronous linear correlation coefficients and the appropriate …

The paper develops a method for forecasting power consumption at various levels of aggregation.

ISVAE enhances interpretability in time series clustering using a novel filter bank.

Time series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each …

Change detection (CD) in time series data is a critical problem as it reveal changes in the underlying generative processes driving the time series. Despite having received significant attention, one important unexplored aspect is how to efficiently utilize additional correlated information to improve the detection and…

Multivariate time series are routinely encountered in real-world applications, and in many cases, these time series are strongly correlated. In this paper, we present a deep learning structural time series model which can (i) handle correlated multivariate time series input, and (ii) forecast the targeted temporal sequ…

Proposes interpretable time series classification through extracted features.