Data Science-Forecasting/Time series Using XLMiner,R&TableauForecasting Techniques-Linear,Exponential,Quadratic Seasonality models, Autoregression, Smooting, Holts, Winters Method
Created by ExcelR Solutions
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What you'll learn
- Learn about different types of approaches using XLminer, R and Tableau
- Learn about the Forecasting Importance ,Forecasting Strategy which includes Defining goal, Data Collection, Exploratory Data Analysis, Partition Series, Pre-process Data, Forecast Methods, using various Plots.
- Learn about scatter diagram, correlation coefficient, confidence interval, which are all required for implementing forecasting techniques
- Learn about the various error measures such as ME, MAD, MSE, RMSE, MPE, MAPE, MASE
- Learn about Model based Forecasting Techniques such as Linear, Exponential, Quadratic, Additive Seasonality, Multiplicative Seasonality, etc.
- Learn about Auto Regressive Models for using errors to further strengthen the forecasting model used & also learn about Random walk & how to identify the same
- Learn about Data Driven approaches such as Moving Average, Simple Exponential Smoothing, Double Exponential Smoothing / Holts, Winters / HoltWinters
- Download XLminer, R , RStudio before starting this tutorial
- Download datasets folder in zip file which is uploaded in Session 1