Forecasting using ARIMA
Sometimes, you will have the need to forecast future values of a time series. For example, this could be a requirement to estimate the next several months of active IoT devices; or, it could be a need to project the usage hours of remote oil well pumps. One of the most popular methods to forecast time series is AutoRegressive Integrated Moving Average (ARIMA).
ARIMA is not one model but a collection of related methods that attempt to describe autocorrelations in the data in order to forecast future values. ARIMA is a combination of moving average and autoregressive techniques. Autoregressive means that the forecasting of future values of a variable is based on the linear combination of the past values of variables.
ARIMA incorporates both trend and seasonality effects into future forecasts. It can model both seasonal and nonseasonal data with a range of methods.
Using R to forecast time series IoT data
The forecast package contains ARIMA functions in R. You can install...
