Stack Index Prediction Using Time-Series Analysis

TL;DR

This paper presents a machine learning-based approach to analyze and predict trends in technology domains using time-series data, demonstrating high accuracy in forecasting the Stackindex model's effectiveness.

Contribution

It introduces the Stackindex model, a novel forecasting tool that leverages time-series analysis and machine learning to predict technology trend trajectories.

Findings

Stackindex accurately forecasts tech domain trends.

Certain concepts like Python and machine learning show consistent uptrends.

The model is a viable tool for future technology trend prediction.

Abstract

The Prevalence of Community support and engagement for different domains in the tech industry has changed and evolved throughout the years. In this study, we aim to understand, analyze and predict the trends of technology in a scientific manner, having collected data on numerous topics and their growth throughout the years in the past decade. We apply machine learning models on collected data, to understand, analyze and forecast the trends in the advancement of different fields. We show that certain technical concepts such as python, machine learning, and Keras have an undisputed uptrend, finally concluding that the Stackindex model forecasts with high accuracy and can be a viable tool for forecasting different tech domains.