Learn about course introduction, which briefly explains about the concepts to be discussed through out the program. 

Learn about who does the forecasting and about the dataset to be used for forecasting. Also learn about the various notations which are pivotal for strong foundation.

Learn about the 8 steps in brief for successful forecasting. Also deep dive on the first step which is Defining Goal, which is the single most important step of the 8-step forecasting strategy.

 Learn about step-2, Data collection in the 8-step forecasting strategy. Also you would gain in-depth knowledge on exploring the data series, which happens to be the 3rd step of the 8-step process. 

Learn about the various components of the forecasting data series including Seasonal, Trend & Random components. 

Learn the rudimentary visualization used in forecasting; Scatter plot. Time Plot, Lag plot. Understand the correlation function, Standard error for lagged plot    

Understanding the ill effects of incorrect visualizations by reduced granulation or transformation. Why we need plots and their uses

Understanding forecast error through various plots, various measure of forecast errors and its dichotomies

Preliminaries to forecasting; pre–processing, incorporating additional variables, data Partitioning in XL Miner

Types of forecasts; Naïve forecast, distribution of error identifying which is more desirable.   

Types of errors its strengths and weaknesses, challenges with data quality, forecasting prediction interval for normal and non–normal data,

Introduction to two main methods of forecasting, Choice of the model based on Data Volatility

Various model based approaches to forecasting, Performing a linear Model forecast using XL Miner, Walk through the various steps involved in Linear Model.

Walk through the steps in performing exponential forecast, Quadratic forecasting, Additive seasonality forecast using XL Miner

Walk through the steps in performing Additive seasonality with trend forecast and multiplicative seasonality; Configuring visually the best model and ratifying with the least error model; Combining the training and Validation data to run the final model

Addressing irregular components, building econometric models based on External information and domain knowledge

A complete recap of all the topics related to forecasting covered so far

Packages in R for reading file; creating a file access pop up using R script; create a data frame with dummy variable for seasonality; creating & assigning new variable to a data frame as a part of pre-processing; evaluating various prediction models for forecasting namely Linear Model, Exponential Model, Quadratic Model (Polynomial trend with two degrees); performing the predict function on the test data for each of the models and evaluating the RMSE(Root Mean Square Error)

Various other model based prediction strategies viz, Additive seasonality, Additive seasonality with Quadratic trend, Multiplicative seasonality; and evaluating for the model with the least RMSE; Running the model with least error using the complete data base for making the prediction; performing an ACF plot on the residuals from the model; Auto regressive model AR(1) on the residuals; check for significant information left over in the residuals of residuals

Features of autoregressive models, Detailed study of residuals and its leftover information, Forecasting errors by AR model using the principle of Parcimony, iterative study of ‘residuals of residuals’ till no further information can be gleaned, Building the final model by plugging in the error factor

The disadvantages and limitations of Model Based approaches, the underlying principle of model based and data based approach to forecasting

Naïve technique, Moving average approach with adequate ‘window width’ for smoothing the data, Key takeaways from the moving average approach; two types of calculations for moving averages and the math behind it

Types of Exponential smoothing and their facets, the formula for Simple exponential smoothing considering decreasing weights for older data, understanding under smoothing and over smoothing

Hands on with the Holt’s method with level and trend, hands on with Winter’s method with level trend and seasonality, importance of period for encompassing the seasonality in the Winter’s method, Forecasting using the model with the least error

De-seasoning the data using seasonal indexes and re–seasoning it using the same

Computing the Moving average even window width and walk thru of the steps in computing the seasonal indexes, Normalizing the seasonal indexes and drawing inferences for these, computing the de seasoned data using the respective seasonal indexes  

Using the Sine and cosine component predictors for capturing the seasonality for daily data, logit regression using odds, Walk through the steps of using XL Miner for performing the Logistic regression

The various packages needed for forecasting; Loading the data on to R and converting the data to time series data; Splitting the data to training and validation data; Visualization of the time series data for analysis

Performing simple moving average using ARIMA; Selecting the appropriate MA value based on the Seasonality; performing the forecast() function and the accuracy function to check errors for training and test data; Performing simple exponential smoothing using only the level component of the time series data; Building Double exponential smoothing model using the level and trend component of the time series data; performing Holts winters model using level, trend and seasonality;

Performing the various exponential smoothing models using the HoltsWinters() command, so as to determine the optimum alpha, beta and gamma values corresponding the Level, Trend and seasonality; Performing the various smoothing techniques using commands line ses() for Simple exponential smoothing, holt() for Holts and hw() for holt Winters technique; Determining the best model based on the MAPE (Mean absolute percent error); Running the best model to from the list on the total data and forecasting one full cycle of seasonality

Forecasting in Tableau using exponential smoothing; choosing date dimension in the column shelf and value dimension in the rows self; Selecting the analytics pane to choose the forecast option for obtaining the forecasts; Understanding the various options within the forecast; Viewing the errors of the forecast; forecasting for non time series numerical data; The eight types of forecasts models;