Purchase and Analytics for Grace Marketing

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In recent years development of computer systems were able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data is known as machine learning.In this phase sales of different lubricants were predicted using a multivariate time series forecasting algorithm.Previously it showed that the model was accurate in predicting the engine oil sales for a particular time.Using Regressions the accuracy of sales prediction was less (74%) and the models like SVM and Random forest were showing signs of over fitting.The accuracy obtained in the multivariate time series forecasting was good than other algorithms.Time series algorithms are used extensively for forecasting time-based data.In time series ARIMA,SARIMA and SARIMAX are the common methods to forecast time based data.SARIMAX are efficient in forecasting data which has seasonality trends than ARIMA which are good in forecasting data which is stationary in nature Time series methods are extensively used for forecasting time based data.In time series ARIMA,SARIMA and SARIMAX are the common methods to forecast tie based data.ARIMA is the abbreviation of Auto Regressive Integrated Moving Average a model which explains a given time series model based on its lags and other values.SARIMAX is the abbreviation of Seasonal Auto Regressive Integrated Moving Average with Xegeneous variables. ARIMA model is best for forecasting stationary time series data and SARIMAX is used for forecasting values which is seasonal in nature.