Business Forecasting Notes
Subject Overview
The fourth Business Analytics group elective, covering forecasting and demand analysis fundamentals, market research techniques and regression forecasting, time series analysis and ARIMA models, and qualitative forecasting techniques and forecast accuracy. A 4-credit elective theory paper.
Subject at a glance
4
Units
27
Topics
4
Credits
4
Infographics
Unit-wise Syllabus
4 units — open any unit for detailed topic-wise notes, diagrams and important exam questions
- Significance of demand analysis and forecasting
- determinants of demand
- elasticity
- revenue and profit estimation
- forecasting technique selection
- purpose of forecast and types of users
Qualitative
Expert opinion, Delphi, surveys
Time series
Moving averages, smoothing, ARIMA
Causal
Regression, econometric models
Barometric
Leading indicators
Study guide
This unit covers demand analysis and forecasting, determinants, elasticity, revenue and profit estimation and choosing a technique. Match each forecasting need to a method.
- Marketing research techniques
- consumer surveys and market experiments
- statistical estimation
- variable identification
- time series and cross-sectional data
- regression model specification and interpretation
- forecasting with regression models
- 1
Identify variables
- 2
Collect time series or cross-section data
- 3
Specify the model
- 4
Estimate coefficients
- 5
Test assumptions
Multicollinearity, autocorrelation
- 6
Forecast and check accuracy
Study guide
You study research techniques, surveys and market experiments, statistical estimation, regression specification and interpretation and forecasting with regression. Fit and interpret a simple regression.
- Smoothing and extrapolation of time series
- seasonal adjustment
- autocorrelation function
- stationarity
- random walk
- linear time series (moving average, autoregressive, ARIMA models, Box-Jenkins methodology)
- ARCH/GARCH volatility modeling
- 1Check stationarity
Difference if needed
- 2Identify p and q
ACF and PACF
- 3Estimate the ARIMA model
- 4Diagnose residuals
Should be white noise
- 5Forecast
Study guide
You study smoothing, extrapolation, seasonal adjustment, autocorrelation, stationarity, random walks, MA, AR, ARIMA, Box-Jenkins and ARCH/GARCH. Difference a series to make it stationary.
- Survey and opinion polling techniques
- exponential smoothing
- barometric techniques
- leading/lagging/coincident economic indicators
- diffusion and composite indexes
- accuracy of forecasts
- use of forecasting software
MAD
Σ abs(actual − forecast) / n
MSE
Σ (actual − forecast)² / n
MAPE
Σ abs(error / actual) / n × 100
Exponential smoothing
Ft+1 = α At + (1 − α) Ft
Study guide
You study surveys, opinion polls, exponential smoothing, barometric techniques, leading and lagging indicators, composite indexes, forecast accuracy and software. Compare forecast errors across methods.
Why Business Forecasting Matters
Good forecasts underlie budgets, inventory, staffing and strategy, and forecasting skills are valued in finance, planning and analytics roles.
How to Prepare for Business Forecasting
- Forecast a real series such as sales or prices with two methods.
- Learn accuracy measures such as MAPE.
- Use Excel or R for ARIMA.
Business Forecasting: Frequently Asked Questions
What is stationarity?
A time series is stationary if its statistical properties, such as mean and variance, do not change over time.