Time Series Forecasting: Obtaining Long Term Trends with Self-Organizing Maps

TL;DR

This paper introduces a novel time series forecasting method that leverages double application of Kohonen self-organizing maps to predict multi-dimensional long-term trends, expanding their use beyond traditional classification tasks.

Contribution

The paper presents a new forecasting approach using Kohonen maps specifically tailored for long-term, multi-dimensional trend prediction in time series data.

Findings

Effective long-term trend prediction demonstrated

Method applicable to multi-dimensional data

Practical applications validate approach

Abstract

Kohonen self-organisation maps are a well know classification tool, commonly used in a wide variety of problems, but with limited applications in time series forecasting context. In this paper, we propose a forecasting method specifically designed for multi-dimensional long-term trends prediction, with a double application of the Kohonen algorithm. Practical applications of the method are also presented.