Data Science-Forecasting/Time series Using XLMiner,R&Tableau,

Data Science-Forecasting/Time series Using XLMiner,R&Tableau,

Data Science-Forecasting/Time series Using XLMiner,R&Tableau,
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
  • Requirements
  • Download XLminer, R , RStudio before starting this tutorial
  • Download datasets folder in zip file which is uploaded in Session 1

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