arXiv research

Paper introduces a new method for classifying interval-valued time series.

BCI provides calibrated prediction intervals for time series forecasts.

In this paper, we describe a newly discovered statistical property of time series data for daily price changes. We conducted quantitative investigation of the {\it calm-time intervals} of price changes for 800 companies listed in the Tokyo Stock Exchange, and for the Nikkei 225 index over a 27-year period from January …

Proposes adaptive method for classifying interval-valued time series.

Traditional approaches focus on finding relationships between two entire time series, however, many interesting relationships exist in small sub-intervals of time and remain feeble during other sub-intervals. We define the notion of a sub-interval relationship (SIR) to capture such interactions that are prominent only …

Paper proposes new method for time series confidence intervals using LSTM.

Energy markets and the associated energy futures markets play a crucial role in global economies. We investigate the statistical properties of the recurrence intervals of daily volatility time series of four NYMEX energy futures, which are defined as the waiting times $τ$ between consecutive volatilities exceeding a gi…

Develops efficient time series prediction intervals.

We present new methods for batch anomaly detection in multivariate time series. Our methods are based on maximizing the Kullback-Leibler divergence between the data distribution within and outside an interval of the time series. An empirical analysis shows the benefits of our algorithms compared to methods that treat e…

The distribution of recurrence times or return intervals between extreme events is important to characterize and understand the behavior of physical systems and phenomena in many disciplines. It is well known that many physical processes in nature and society display long range correlations. Hence, in the last few year…

We develop a model to cluster time-series data with interval censoring, improving disease phenotyping.

New method combines HQR and WACI for better time series prediction intervals.

Modeling disease progression using irregular time intervals in EHRs.

BC-ACI corrects time series forecast bias, improving prediction intervals.

Time-series data is being increasingly collected and stud- ied in several areas such as neuroscience, climate science, transportation, and social media. Discovery of complex patterns of relationships between individual time-series, using data-driven approaches can improve our understanding of real-world systems. While …

ICODEN models survival data with interval-censored times using neural networks and ODEs.

New method explains anomalies in multivariate time series data.

Neural network learns kernel functions for survival analysis and prediction intervals.

Study bounds for European basket call options in a discrete-time market model with price jumps.

This work tackles fitting Hawkes processes to interval-censored data.

TCP provides well-calibrated prediction intervals for nonstationary time series.

Boosting methods for interval-censored data improve predictive accuracy in survival analysis.

This paper introduces time-uniform CLT-based confidence intervals for statistical inference.

Prediction intervals are a valuable way of quantifying uncertainty in regression problems. Good prediction intervals should be both correct, containing the actual value between the lower and upper bound at least a target percentage of the time; and tight, having a small mean width of the bounds. Many prior techniques f…

LPCI provides valid prediction intervals for longitudinal data.

Develops active intervals for geodesics in Teichmüller space.

Variational autoencoder models dynamic latent graphs for neural point processes.

Novel method for time-series prediction with tighter confidence intervals.

New methods for ordinal classification of interval-valued data and functional data.

Exponential smoothers are a simple and memory efficient way to compute running averages of time series. Here we define and describe practical properties of exponential smoothers for signals observed at constant and variable intervals.

Study analyzes derivative-free loss method for solving PDEs and fluid problems.

Sequential pattern mining is an interesting research area with broad range of applications. Most prior research on sequential pattern mining has considered point-based data where events occur instantaneously. However, in many application domains, events persist over intervals of time of varying lengths. Furthermore, tr…

The method learns to partition event time space for better prediction.

It will be discussed the statistics of the extreme values in time series characterized by finite-term correlations with non-exponential decay. Precisely, it will be considered the results of numerical analyses concerning the return intervals of extreme values of the fluctuations of resistance and defect-fraction displa…

Develops a method for multivariate time series prediction intervals.

We calculate eigenvector overlaps between intersecting time periods of covariance matrices.

This article provides the first procedure for computing a fully data-dependent interval that traps the mixing time $t_{\text{mix}}$ of a finite reversible ergodic Markov chain at a prescribed confidence level. The interval is computed from a single finite-length sample path from the Markov chain, and does not require t…

In this note, we first prove that the solution of mean curvature flow on a finite time interval $[0,T)$ can be extended over time $T$ if the space-time integration of the norm of the second fundamental form is finite. Secondly, we prove that the solution of certain mean curvature flow on a finite time interval $[0,T)$ …

CIR method constructs efficient prediction intervals with guaranteed coverage.

Methods for prediction and tolerance intervals in non-normal models.

We investigate the growth optimal strategy over a finite time horizon for a stock and bond portfolio in an analytically solvable multiplicative Markovian market model. We show that the optimal strategy consists in holding the amount of capital invested in stocks within an interval around an ideal optimal investment. Th…

Stock price change in financial market occurs through transactions in analogy with diffusion in stochastic physical systems. The analysis of price changes in real markets shows that long-range correlations of price fluctuations largely depend on the number of transactions. We introduce the multiplicative stochastic mod…

Time series data that are not measured at regular intervals are commonly discretized as a preprocessing step. For example, data about customer arrival times might be simplified by summing the number of arrivals within hourly intervals, which produces a discrete-time time series that is easier to model. In this abstract…

In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process. In particular, there may be certain time intervals exhibiting rapid changes and others in which changes are slow. If such time-varying smoothness is not accounted for, one can obtain misleading inf…

An average instantaneous cross-correlation function is introduced to quantify the interaction of the financial market of a specific time. Based on the daily data of the American and Chinese stock markets, memory effect of the average instantaneous cross-correlations is investigated over different price return time inte…

We propose an approach to explain fluctuations in time intervals of financial markets data from the view point of the Gini index. We show the explicit form of the Gini index for a Weibull distribution which is a good candidate to describe the first passage time of foreign exchange rate. The analytical expression of the…

New online conformal prediction methods minimize strongly adaptive regret and achieve near-optimal coverage.

CPTD improves prediction intervals in time series regression with cross-sectional data.