Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based Data

TL;DR

This paper presents a 2D time series forecasting model that incorporates cohort behavior, improving accuracy and adaptability in small data environments for strategic decision-making.

Contribution

It introduces a novel 2D time series approach that effectively models cohort dynamics, enhancing forecasting performance over traditional methods.

Findings

Superior accuracy on multiple real-world datasets

Enhanced adaptability in small data scenarios

Provides valuable strategic insights

Abstract

This paper introduces a novel two-dimensional (2D) time series forecasting model that integrates cohort behavior over time, addressing challenges in small data environments. We demonstrate its efficacy using multiple real-world datasets, showcasing superior performance in accuracy and adaptability compared to reference models. The approach offers valuable insights for strategic decision-making across industries facing financial and marketing forecasting challenges.