Data Science-Forecasting/Time series Using XLMiner,R&Tableau
Forecasting Techniques-Linear,Exponential,Quadratic Seasonality models, Autoregression, Smooting, Holts, Winters Method
4.19 (90 reviews)
1,355
students
6.5 hours
content
Mar 2018
last update
$49.99
regular price
What you will 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
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1062344
udemy ID
1/5/2017
course created date
11/22/2019
course indexed date
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