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Related Concept Videos

Prediction Intervals01:03

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. 
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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...
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
Poisson Probability Distribution01:09

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
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Related Experiment Videos

Event-preserving feature engineering for intermittent demand forecasting using SHOS.

B Sendhil Nathan1,2, Veera Siva Reddy B3, C Chandrasekhara Sastry4,5

  • 1Department of Mechanical Engineering, Indian Institute of Information Technology Design and Manufacturing Kurnool (IIITDM Kurnool), Centrally Funded Technical Institute (CFTI), Institute of National Importance under Ministry of Education, Govt. of India, Kurnool, 518008, Andhra Pradesh, India.

Scientific Reports
|June 13, 2026
PubMed
Summary
This summary is machine-generated.

Intermittent demand forecasting is improved by the new Smoothed Hybrid Occurrence-Size (SHOS) framework. This statistically grounded feature engineering approach enhances accuracy and stability for sparse demand data in supply chains.

Keywords:
Event preservationFeature engineeringIntermittent demand forecastingMachine learningSHOSSparse time series

Related Experiment Videos

Area of Science:

  • Supply Chain Management
  • Operations Research
  • Statistical Modeling

Background:

  • Intermittent demand forecasting presents significant challenges in large-scale supply chains due to data sparsity and irregular patterns.
  • Existing research often prioritizes architectural complexity over statistically grounded feature representation for sparse demand.
  • Effective feature engineering is crucial for improving forecasting performance under intermittent demand conditions.

Purpose of the Study:

  • Introduce the Smoothed Hybrid Occurrence-Size (SHOS) framework for event-preserving feature engineering in intermittent demand forecasting.
  • Develop adaptive, series-specific statistical representations that capture demand occurrence and size.
  • Integrate SHOS as a feature generation mechanism into supervised learning pipelines to enhance forecasting models.

Main Methods:

  • Decomposed intermittent demand into latent occurrence probability and conditional demand size components.
  • Applied sparsity-aware exponential smoothing for adaptive, series-specific feature generation.
  • Evaluated the SHOS framework using a large-scale automotive aftermarket dataset with extensive time series data.
  • Utilized rolling-window cross-validation, signal-preservation analysis, and robustness studies.

Main Results:

  • SHOS-augmented tree-based models significantly improved forecasting accuracy and stability compared to raw-feature baselines.
  • The SHOS-augmented LightGBM model reduced Mean Absolute Error by approximately 46.3% and improved Weighted Mean Absolute Percentage Error by over 40%.
  • SHOS successfully preserved crucial demand characteristics like event timing, peak structure, and frequency-domain information, often lost in conventional methods.

Conclusions:

  • Statistically grounded feature representations, like SHOS, can substantially enhance sparse-demand learning behaviors.
  • The SHOS framework demonstrates the effectiveness of event-preserving, representation-oriented forecasting for highly intermittent demand.
  • Feature engineering plays a vital role, complementing architectural complexity in addressing intermittent demand forecasting challenges.