PSF: a good alternative to the ARIMA method for seasonal univariate time series forecasting

Time series analysis plays an important role in numerous applications

There are limited univariate time series forecasting methods and ARIMA is one of the leading methods in the domain

PSF, a possible alternative for ARIMA method for seasonal univariate time series forecasting

This post describes and demonstrates the PSF method and its R package

Challenges in Univariate Time Series Analysis:

Time series analysis plays a crucial role in various fields such as healthcare, economics, finance, environment, climate, agriculture, research, energy, and many others. The scope of time series analysis is vast, encompassing tasks like predictions, univariate forecasts, missing value imputations, outlier detection, time series transformation, and data cleaning. In most applications, the ultimate goal is to achieve accurate predictions or forecasts.

Among these tasks, univariate time series forecasting stands out as particularly challenging. This process requires a deep understanding of the underlying patterns within a single time series, along with the ability to replicate and extrapolate those patterns into the future. In contrast, multivariate forecasting benefits from additional variables that can improve the predictive process, often making it easier to achieve accurate results. As a result, there are fewer methodologies available for…