DATA ANALYSIS : TOP STATISTICAL SOFTWARE FOR UNIVARIATE TIME SERIES FORECASTING

Univariate time series analysis is the process of analyzing a single time series data to understand its characteristics, patterns, and trends. There are several software options available for univariate time series analysis, including:

(i) R: 

R is a popular open-source programming language and software environment for statistical computing and graphics. It offers a wide range of packages for univariate time series analysis, such as the "forecast" package, "tseries" package, "prophet" package, etc.

R is a popular open-source programming language and software environment for statistical computing and graphics. It offers a wide range of packages for time series forecasting, some of the most popular ones are:

"forecast": The forecast package provides methods and tools for displaying and analyzing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modeling.

"tseries": The tseries package provides tools for time series analysis and forecasting, including the popular Box-Jenkins ARIMA method, and various other time series models.

"prophet": The prophet package provides an easy-to-use tool for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects.

"ets": The ets package provides an easy-to-use tool for forecasting time series data based on exponential smoothing models, including ETS (Error, Trend, Seasonality) and ETS (Error, Trend, Seasonality, Regression) models.

"arima": The arima package provides functions for fitting and analyzing ARIMA models, including seasonal decomposition, forecasting, etc.

"fable": The fable package provides tools for modeling and analyzing time series data using functional data analysis and exponential smoothing frameworks.

These are some of the most popular packages available in R for time series forecasting,

Application of R Programming Language 

(ii) Python: 

Python is another popular open-source programming language that is widely used for data analysis and machine learning. It offers a wide range of libraries for univariate time series analysis, such as statsmodels, pandas, fbprophet, etc.

SAS: SAS is a software suite for data management, advanced analytics, and business intelligence. It has a rich set of tools for univariate time series analysis such as time series forecasting, decomposition, and modeling.

SPSS: SPSS is a software package used for statistical analysis in social sciences. It has a time series module that allows users to perform decomposition, forecasting, and trending analysis.

EViews: EViews is a software package for econometric and time series analysis. It offers a wide range of tools for univariate time series analysis, such as time series regression, forecasting, and model identification.

STATA: STATA is another software package for statistical analysis, it has a rich set of tools for univariate time series analysis, such as unit root test, cointegration test, and forecasting.