A time series is a sequence of observations recorded at regular time intervals. Depending on the frequency of observations, a time series may typically be hourly, daily, weekly, monthly, quarterly, or annual.
Time series forecasting is basically machine learning modeling for Time Series data (years, days, hours…etc.)for predicting future values using Time Series modeling.
In this case study, time is the independent variable, while the dependent variable is Sales data of a superstore.
Using ARIMA and SARIMA time series models, I forecasted the future sales of a superstore by leveraging historical data spanning four years, from 2015 to 2018.