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Time-Series Graph

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...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Survival Tree

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.
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End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

Discrete-Time Fourier Series

Discrete-Time Fourier Series

The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
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Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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Related Experiment Video

Updated: Nov 27, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Deep Learning for Time Series Forecasting: A Survey.

José F Torres¹, Dalil Hadjout², Abderrazak Sebaa^3,4

¹Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain.

|December 4, 2020

Summary

This summary is machine-generated.

Deep neural networks offer high accuracy for time series forecasting. This review details common deep learning architectures and practical considerations, identifying research gaps for future advancements.

Keywords:

big data deep learning time series forecasting

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Last Updated: Nov 27, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Artificial Intelligence
Machine Learning
Data Science

Background:

Time series forecasting is a critical research area with increasing interest.
Deep neural networks (DNNs) are highly effective machine learning methods for big data problems.
DNNs demonstrate significant accuracy across various application domains.

Purpose of the Study:

To formulate the time series forecasting problem and its mathematical underpinnings.
To review prevalent deep learning architectures for time series prediction.
To identify research gaps and inspire future work.

Main Methods:

Description of feed forward networks.
Detailed analysis of recurrent neural networks (RNNs), including Elman, LSTM, GRU, and bidirectional variants.
Explanation of convolutional neural networks (CNNs) for time series.

Main Results:

Discussion of the advantages and limitations of each deep learning architecture.
Review of practical aspects like hyper-parameter tuning and framework selection.
Overview of successful applications of these architectures in diverse fields.

Conclusions:

Deep learning architectures are powerful tools for time series forecasting.
Understanding architectural nuances and practical implementation is key to success.
Identified research gaps offer avenues for novel contributions in time series analysis.