CoolStatsBlog » Time Series

Say you have a dataset, where each row has a date or time, and something is recorded for that date and time. If each row is a unique date – great! If not, you may have rows with the same date,...

https://coolstatsblog.com/2013/11/15/daily-tallies-on-r/

There are different types of data on R. I use type here as a technical term, rather than merely a synonym of “variety”.  There are three main types of data: Numeric: ordinary numbers Charac...

https://coolstatsblog.com/2013/08/26/types-of-data-in-r/

Suppose you have decided on a suitable model for a timeseries. In this case, we have selected an ARIMA(2,1,3) model, using the Akaike Information Criteria (AIC) as our sole criterion for choosing...

https://coolstatsblog.com/2013/08/19/forecasting-a-timeseries/

The Akaike Information Critera (AIC) is a widely used measure of a statistical model. It basically quantifies 1) the goodness of fit, and 2) the simplicity/parsimony, of the model into a single s...

https://coolstatsblog.com/2013/08/14/using-aic-to-test-arima-models-2/

In my previous post, I wrote about using the autocorrelation function (ACF) to determine if a timeseries is stationary. Now, let us use the ACF to determine seasonality. This is a relatively stra...

https://coolstatsblog.com/2013/08/11/how-to-use-autocorreation-function-acf-to-determine-seasonality/

The Autocorrelation function is one of the widest used tools in timeseries analysis. It is used to determine stationarity and seasonality. Stationarity: This refers to whether the series is “go...

https://coolstatsblog.com/2013/08/07/how-to-use-the-autocorreation-function-acf/