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Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

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Related Experiment Video

Updated: May 8, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

Deep Time Series Models: A Comprehensive Survey and Benchmark.

Yuxuan Wang, Haixu Wu, Jiaxiang Dong

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 6, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Deep learning models offer advanced time series analysis, but their effectiveness varies by task. This study introduces Time Series Library (TSLib) for benchmarking deep time series models across diverse applications.

    Related Experiment Videos

    Last Updated: May 8, 2026

    Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
    12:26

    Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

    Published on: October 11, 2016

    Area of Science:

    • Computer Science
    • Data Science
    • Machine Learning

    Background:

    • Time series data, crucial in many fields, presents unique modeling challenges due to complex patterns and trends.
    • Traditional statistical methods are increasingly supplemented by deep learning for enhanced time series analysis.

    Purpose of the Study:

    • To review deep time series models focusing on basic modules and architectures.
    • To introduce Time Series Library (TSLib) as a benchmark for evaluating deep time series models.
    • To provide insights into selecting appropriate models for specific time series analysis tasks.

    Main Methods:

    • A comprehensive literature review of deep time series models, categorized by modules and architectures.
    • Development and release of Time Series Library (TSLib), a benchmark encompassing 41 models and 30 datasets.
    • Empirical evaluation of 16 popular and 6 advanced time series foundation models using TSLib across 5 tasks.

    Main Results:

    • Model performance is task-specific, with certain structures excelling in distinct analytical applications.
    • TSLib facilitates fair and comprehensive benchmarking of deep time series models.
    • Evaluation highlights the varying suitability of different deep learning architectures for time series tasks.

    Conclusions:

    • Deep time series models require careful selection based on the specific analytical task.
    • TSLib serves as a valuable resource for researchers and practitioners in time series analysis.
    • Findings guide the adoption and development of effective deep learning solutions for time series data.