Course Description
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 a time series analysis. Forecasting techniques will be applied for time series data. Forecasting Analytics is considered one of the major branches in big data analytics.
Managers often have to take decisions in an uncertain environment and often find themselves in a bad situation due to a lack of skills in applying the right analytical techniques on the data. Forecasting techniques help companies save millions of dollars by adjusting their production schedules and other plans. Forecasting techniques on univariate and multivariate time series analysis have 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
Course Curriculum
Things You Will Learn
- Forecasting and its need
- Types of Forecasting
- Steps involved in Forecasting
- Types of Plots – Scatter Plot, Time Plot, Lag Plot, ACF Plot
- Autocorrelation and Standard Error
- Common pitfalls of plots and Aspect ratio
- Time Series Components – Trend, Cyclical, Seasonal and Irregular
- Ljung box test for identifying randomness
- Forecasting errors and 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 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 and Combine
- 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 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 and Weighted Regression
- 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 and Sigmoid
- ARCH & GARCH models
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