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Prediction Intervals

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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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Predicting Products: Substitution vs. Elimination

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Related Experiment Video

Updated: Apr 5, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Sequence Prediction With Sparse Distributed Hyperdimensional Coding Applied to the Analysis of Mobile Phone Use

Okko J Rasanen, Jukka P Saarinen

IEEE Transactions on Neural Networks and Learning Systems

|August 19, 2015

Summary

This study introduces a novel sparse hyperdimensional coding method for sequence prediction. This incremental approach efficiently captures variable-order structures, outperforming traditional n-grams and mixed-order Markov models in mobile usage pattern prediction.

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Last Updated: Apr 5, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Area of Science:

Machine Learning
Signal Processing
Data Mining

Background:

Sequence prediction is crucial for signal processing and machine learning.
Traditional methods like n-grams and Markov models have limitations in capturing complex temporal structures.
Existing techniques often require significant computational resources or batch training.

Purpose of the Study:

To present a novel sequence prediction method using sparse hyperdimensional coding.
To demonstrate the method's ability to utilize higher-order temporal structures effectively.
To enable real-time online learning and prediction with limited computational resources.

Main Methods:

Developed a sequence prediction technique based on sparse hyperdimensional coding.
Focused on balanced utilization of higher-order temporal structures within the coding scheme.
Implemented an incremental learning approach for real-time processing.

Main Results:

The proposed sparse hyperdimensional predictor successfully captured variable-order structures in mobile usage sequences.
Outperformed n-grams and mixed-order Markov models in unweighted average recall.
Achieved weighted average recall comparable to mixed-order Markov chains without batch training.

Conclusions:

Sparse hyperdimensional coding offers an efficient and effective approach for sequence prediction.
The incremental nature of the method is suitable for real-time applications with limited resources.
This method provides a competitive alternative to existing sequence modeling techniques.