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 an important role in numerous applications such as healthcare, economics, finance, environment, climate, agriculture, research, energy, and many more. There are several aspects of time series analysis, which includes predictions, univariate forecasts, missing value imputations, outlier detections, time series transformation, and cleaning, etc. In most of the applications, the final goal is to achieve an accurate prediction or forecasting results. Out of these, forecasting a univariate time series is one of the challenging tasks, since such a process needs to understand, recreate, and extrapolate the patterns available in the targeted time series itself. Whereas, in the multivariate prediction process, there can be the availability of several variables with which predicting process usually becomes easier. This is the reason that there are very few methodologies are available for univariate time series forecasting than the multivariate prediction ones.

The ‘Forecast’ package in R is a boon for time series analysis, which provides several features and benchmarks including ARIMA and ETS methods and corresponding user-friendly interface. In this blog, I am introducing and demonstrating a very interesting R package for time series forecasting, which can be an excellent alternative and replacement for the benchmarked forecasting methods, especially for the time series with seasonal characteristics.

Yes, it is the ‘Pattern Sequence-based forecasting algorithm’, popularly known as ‘PSF’. It is a successful forecasting method based on the assumption that there exist pattern sequences in the time series. It was proposed in the year 2011 and published here.

#r-package #time-series-analysis #psf #arima #data analysis