Due to the sweeping digitalization of processes, increasingly vast amounts of time series data are being produced. Accurate classification of such time series facilitates decision making in multiple domains. State-of-the-art classification accuracy i...
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Non-stationary time series with non-linear trends are frequently encountered in applications. We consider here the feasibility of accurately forecasting the signals of multiple such time series considering jointly when the number of historic samples...
In the last five years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series...
Time series classification (TSC) is home to a number of algorithm groups that utilise different kinds of discriminatory patterns. One of these groups describes classifiers that predict using phase dependant intervals. The time series forest (TSF) cla...
A heuristic law widely used in fluid dynamics for steady flows states that the amount of a fluid in a control volume is the product of the fluid influx and the mean time that the particles of the fluid spend in the volume, or mean residence time. We...
In atmospheric chemistry, a parameter called residence time can be defined for each gas as T=M/F, where M represents the average mass in the atmosphere and F is the total average influx or outflux, which in time averages are equal. In this paper we e...
Haluk Levent, the Turkish rock star who was named one of TIME's 100 Most Influential People of 2023, has refused to attend a gala in New York City on April 26 celebrating the list, saying he can't accept the criticism of the Turkish government in the entry pen…
Intermittent fasting is a pattern of eating based on time limits. For a set time of hours or days, you eat a typical diet. At the end of the set time, you switch to very few or no calories, called fasting. When …
The novel STEVE (i.e., Space-Time-Enclosing Volume Extraction) algorithm is described here for the very first time. It generates iso-valued hypersurfaces that may be implicitly contained in four-dimensional (4D) data sets, such as temporal sequences...
The Courant-Friedrichs-Lewy (CFL) condition is a well known, necessary condition for the stability of explicit time-stepping schemes that effectively places a limit on the size of the largest admittable time-step for a given problem. We formulate and...
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Purpose: To develop and compare normal tissue complication probability (NTCP) models for recurrent brain metastases (BMs) treated with repeat single-fraction stereotactic radiosurgery (SRS), considering time-dependent discounted prior dose. Methods:...
This work studies the effect of hub congestion and time-sensitive demand on a hub-and-spoke location/allocation system. The Hub Location with Congestion and Time-sensitive Demand Problem is introduced, which combines these two main characteristics. O...
The quality of earthquake prediction is usually characterized by a two-dimensional diagram 'n' vs. 'tau', where 'n' is the rate of failures-to-predict and 'tau' is a characteristic of space- time alarm. Unlike the time prediction case, the quantity...
We propose TAMER, a Test-time Adaptive MoE-driven framework for Electronic Health Record (EHR) Representation learning. TAMER introduces a framework where a Mixture-of-Experts (MoE) architecture is co-designed with Test-Time Adaptation (TTA) to joint...
Reflecting the recent rise in Japanese basketball's competitiveness and pivotal outcomes in international tournaments, clutch-time performance has become increasingly critical. We examine clutch-time performance in Japan's B.League using play-by-play...
Audio processors whose parameters are modified periodically over time are often referred as time-varying or modulation based audio effects. Most existing methods for modeling these type of effect units are often optimized to a very specific circuit a...
Time series anomaly detection dataset from an anonymous commercial bank (⭐ 0)
We introduce Hyper-Trees as a novel framework for modeling time series data using gradient boosted trees. Unlike conventional tree-based approaches that forecast time series directly, Hyper-Trees learn the parameters of a target time series model, su...