# Stack Index Prediction Using Time-Series Analysis

arXiv: https://arxiv.org/abs/2108.08120 · HTML: https://mlsanity.com/paper/2108.08120/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [An Introductory Study on Time Series Modeling and Forecasting](https://mlsanity.com/paper/1302.6613/) (2013)
- [Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases](https://mlsanity.com/paper/2105.03266/) (2021)
- [StackOverflow vs Kaggle: A Study of Developer Discussions About Data Science](https://mlsanity.com/paper/2006.08334/) (2020)
- [Dynamic patterns of knowledge flows across technological domains: empirical results and link prediction](https://mlsanity.com/paper/1706.07140/) (2017)
- [Analyzing programming languages by community characteristics on Github and StackOverflow](https://mlsanity.com/paper/2006.01351/) (2020)

## Abstract (arXiv)

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.