After checking the stationarity, the next step is to select the appropriate model for our data to predict future observations in order to enhance things. For this purpose, a number of models are available. We will discuss the ARIMA model. ARIMA (Auto Regressive Integrated Moving Average) model, which comprises three parts AR, I, and MA,. […]

Time series analysis is a method of examining a set of data points collected over a period of time. Time series analysis implies analysts recording data points at constant intervals over an established span rather than just periodically or arbitrarily. It is simple to attain using the ts() method and certain parameters. Time series use

Stationarity is a primary principle in time series analysis. A time series is considered to be stationary if its statistical aspects, such as mean, variance, and autocorrelation, remain constant through time. To elucidate, the time series does not reflect trends, seasonality, or other regular patterns that alternate with time. Stationarity is essential in time series

Time series and panel are import data sets in certain fields of study like economics, finance, engineering etc. However, these data sets contain missing values, which can lead to biased or inaccurate results. Handling missing data is a crucial aspect of data analysis, particularly in time series and panel data settings where observations may be