Variations on two-parameter families of forecasting functions: seasonal/nonseasonal Models, comparison to the exponential smoothing and ARIMA models, and applications to stock market data

TL;DR

This paper introduces 24 new two-parameter forecasting models that outperform traditional Holt-Winters and ARIMA methods in accuracy, without needing seasonal decomposition, and demonstrates their effectiveness on various real-world datasets including stock market data.

Contribution

The paper presents a novel nonparametric approach to develop 24 two-parameter forecasting models, including seasonal and nonseasonal variants, with superior performance demonstrated on multiple datasets.

Findings

Models outperform Holt--Winters and ARIMA in accuracy.

Models do not require seasonal decomposition.

Effective on stock market data.

Abstract

We introduce twenty four two-parameter families of advanced time series forecasting functions using a new and nonparametric approach. We also introduce the concept of powering and derive nonseasonal and seasonal models with examples in education, sales, finance and economy. We compare the performance of our twenty four models to both Holt--Winters and ARIMA models for both nonseasonal and seasonal times series. We show in particular that our models not only do not require a decomposition of a seasonal time series into trend, seasonal and random components, but leads also to substantially lower sum of absolute error and a higher number of closer forecasts than both Holt--Winters and ARIMA models. Finally, we apply and compare the performance of our twenty four models using five-year stock market data of 467 companies of the S&P500.