Forecasting Training

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About Forecasting Certification Training

Early knowledge is the wealth, even if that knowledge is bit imperfect!!! Wouldn’t you want to unlock the mystery of predicting the stock market? And many of us want to understand how companies are managing their inventory and other resources by forecasting their sales.

Here is the solution in the form forecasting technique also called as time series analysis. Forecasting techniques will be applied for time series data. Forecasting Analytics is considered as one of the major branches in big data analytics.

Managers often have to take decisions in uncertain environment and often find themselves in a bad situation due to lack of skills on applying the right analytical techniques on the data. Forecasting techniques helps companies save millions of dollars by adjusting their production schedules and other plans. Forecasting techniques on univariate and multivariate time series analysishave huge applications across the industries and areas such as Operations management, Finance & Risk management, Retails, Telecom and manufacturing.

Moving Averages and smoothing methods, Box- Jenkins (ARIMA) methodology, Regression with time series data, Holts-Winter, Arch-Garch and Neural Network are the methods widely used for forecasting. Arch-Garch and Neural Networks are the advanced techniques in the forecasting analytics which will be used to model the high frequency data such as stock market and big data.

  • Electricity usage pattern over a period of years in a region
  • Sales of a product over several years
  • Stock market data

Things You Will Learn…

Introduction to Forecasting

  • Forecasting & its need
  • Types of forecasting
  • Steps involved in forecasting
  • Types of plots – Scatter plot, Time plot, Lag plot, ACF plot
  • Autocorrelation & standard error
  • Common pitfalls of plots & Aspect ratio
  • Time series components – Trend, Cyclical, Seasonal, Irregular
  • Ljung box test for identifying randomness

Forecasting Errors

  • Forecasting error & the measures associated with it
    • Mean Error
    • Mean Absolute Deviation
    • Mean Squared Error
    • Root Mean Squared Error
    • Mean Percentage Error
    • Mean Absolute Percentage Error

Forecasting Methods

  • Forecasting methods based on smoothing
    • Moving Average
    • Exponential Smoothing
  • Decomposition of time series into 4 components
    • Additive model
    • Multiplicative model
    • Mixed model
  • Curve fitting – Least square method
  • Simple exponential smoothing (SES)
  • Forecasting strategy – Separate, Forecast, Combine

Smoothing Methods

  • Moving averages
    • Naive model
    • Naive Trend model
    • Simple average model
    • Moving average over k time periods
  • Exponential smoothing
    • Simple exponential smoothing
    • Holt’s version
    • Winter’s modification

Modeling different components

  • Modeling random component
  • Models for stationary time series
    • Autoregressive model (AR)
    • Moving average model (MA)
    • Autoregressive Moving Average (ARMA) model
    • Autoregressive Integrated Moving average (ARIMA) model
  • Building seasonality into ARIMA models
  • Simple Linear, Multiple, Weighted regression

Detecting Anomalies

  • Non-linearity detection
    • Scatter plot
    • Partial residual plot
    • Partial regression plot
  • Non-normality detection
    • Normal plot
    • Jarque-Bera Normality test
  • Transformations
    • Box-Cox
    • Box-Tidwell
  • Growth curve – Trend, Linear, Quadratic, Exponential, Sigmoid
  • ARCH & GARCH models

Forecasting steps involves:

Data manipulation and cleaning
Problem formulation and data collection
Model building and evaluation
Model implementation to generate forecast
Forecast evaluation

Tools You Will Learn…

  • MS-Excel
  • R – Revolution Analytics is recently acquired by Microsoft but still remains to be an open source software

Forecasting Course Introduction Video

Watch our sample e-learning video recorded by industry’s best trainers with extensive subject knowledge expertise and who are considered to be the best trainers of the industry. All the participants will be provided access to our state-of-the-art Learning management system (LMS) at, where one can access end to end course videos at your own pace & convenience sitting back at your home. Videos can be accessed from your desktop, mobile, tablet, etc. Switch back and forth as you choose


  • Forecasting is predicting the future by considering the historical past data. For e.g., companies forecast sales of next quarter by looking into sales of previous quarters. However, data should be in time-series for forecasting the future events.
  • Data arranged in a sequence in an order based on time. For e.g., company sales should be arranged in a time sequence (Jan ‘15, Feb ‘15, March ‘15, April ‘15, May ‘15, June ‘15, July ’15) before forecasting the sales of Aug ’15.
  • Forecasting is used across all industries & sectors. Majorly it is applicable for Financial services & insurance, Retail & in weather forecasting.
  • A lot of tools are used including R, SAS, STATA, MATLAB, Minitab, Excel etc. We at ExcelR teach you forecasting on R which is highly in demand.
  • The detailed course outline is provided on the website. All the forecasting models including AR, MA, ARMA, ARIMA, ARCH & GARCH models are taught.
  • Yes, forecasting techniques are imperative for one to be a successful data scientist.



A good interactive session, he has knowledge, experience and above all skill to make things understand with right examples.He should run program train the Trainer :-)”

Harshad Raorane, Sr. Consultant, Capgemini India



Training Is Excellent!

Amit Burman, Sr. Consultant, Capgemini India


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