fre
À propos de   |   Newsletter
Demand & Supply Chain Management Evolution


     
Détail du document
Langue: English

The Bayesian Approach to Forecasting

 
 
 

Société: Oracle
Étiquette: Demand Planning
Supply Chain Management
Forecasting


The Bayesian approach uses a combination of a priori and post priori knowledge to model time series data. That is, we know if we toss a coin we expect a probability of 0.5 for heads or for tails—this is a priori knowledge. Therefore, if we take a coin and toss it 10 times, we will expect five heads and five tails. But if the actual result is ten heads, we may lose confidence in our a priori knowledge. This may be explained by a change to the coin that was introduced to alter the probability—this is post priori knowledge. Another example of post priori knowledge is future price change or marketing promotion that is likely to alter the forecast.

The main principle of forecasting is to find the model that will produce the best forecasts, not the best fit to the historical data. The model that explains the historical data best may not be best predictive model for several reasons:
• The future may not be described by the same probability as the past. Perhaps neither the past nor the future is a sample from any probability distribution. The time series could be nothing more than a non-recurrent historical record.
• The model may involve too many parameters. Overfitted models could account for noise or other features in the data that are unlikely to extend into the future.
• The error involved in fitting a large number of parameters may be damaging to forecast accuracy, even when the model is correctly specified.

In any of these cases, the model may fit the historical data very well, yet still forecast poorly, illustrating that there is a vast difference between its internal and external validities.

 





lire le Document complet >>
(lien externe)
  
 
 

Autres Documents de
Oracle
en English
:

Avoid Brand Destruction Due to Product Recalls

Driving Outsourced Manufacturing Best Practices with Oracle

Energy Efficiency

From Customer Orders through Fulfillment: Challenges and Opportunities in Manufacturing, High-Tech & Retail

Is Fraud the Only Ailment? Treat Your Procure to Pay Process to a Routine Health Check

Lower Costs, Simplify Processes: Rethink Logistics with Oracle

Managing the Product Value Chain for the Industrial Manufacturing Industry

Maximize Supplier Enablement for Procure­to­Pay Success

Milking Demand

Oracle Agile PLM for the Consumer Packaged Goods Industry

Oracle Agile PLM for the Industrial Manufacturing Industry

Oracle Agile PLM for the Medical Device Industry

Oracle Agile PLM for the Semiconductor Industry

Oracle E-Business Suite In-Memory Cost Management

Oracle Fusion Procurement: Understanding Coexistence Strategies

Oracle In-Memory Logistics Command Center on Oracle Engineered Systems Maximize Performance of your Logistics Network

Oracle Transportation Management on Oracle Engineered Systems: Optimized Performance and Business Value

Overcoming Order Management Complexity in Global Organizations

Overview and Business Value: An Interactive Planning and Analytics Paradigm

Overview and Business Value: Move Planning Closer to the Consumer

Procurement Executive Insights: Key Procurement Issues in 2013

Product Lifecycle Management for the Pharmaceutical Industry

Responding to the Cross-Channel Challenge

Spend Management Best Practices: A Call for Data Management Accelerators

The Adaptive Supply Chain: Postponement for Profitability

Transform Your Distributed Order Management For Improved Fulfillment

Translate Ideas into a Winning Portfolio of Innovations with Oracle Innovation Management

VIDEO: Going Green with Oracle Supply Chain Management