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Expectation-Based Probabilistic Naive Approach for Forecasting Involving Optimized Parameter Estimation.

Sahil Ahuja¹, Abhimanyu Kumar²

¹Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004 India.

Arabian Journal for Science and Engineering

|May 2, 2022

Summary

This summary is machine-generated.

This study introduces a probabilistic forecasting method that learns from data to predict future values. The new technique shows strong performance compared to established methods like ARIMA and Holt-Winters.

Keywords:

Expectation Forecasting Optimization

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Area of Science:

Statistics
Time Series Analysis
Machine Learning

Background:

Traditional forecasting methods often struggle with complex data patterns.
There is a need for adaptable and accurate predictive models.

Purpose of the Study:

To present a novel probabilistic forecasting technique.
To validate its effectiveness against existing methods and real-world data.

Main Methods:

Developed a forecasting approach based on a probabilistic naïve method.
Learned an unknown parameter via error minimization.
Validated the algorithm on test functions, special sequences, and COVID-19 data.

Main Results:

The proposed technique achieved favorable results across all validation datasets.
Demonstrated competitive or superior performance compared to ARIMA and Holt-Winters.
Provided insights into the method's operational dynamics.

Conclusions:

The probabilistic forecasting technique is effective and versatile.
It offers a promising alternative for time series prediction, including in critical applications like epidemiological forecasting.