Time Series Models: Approaches to Forecasting : A Tutorial

Time Series Models

Time Series Models
Time Series Components of Demand…
Basic Idea Behind Time Series Models
Moving Average Models
Table of Forecasts & Demand Values…
… and Resulting Graph

What Are Time Series Models

• Quantitative forecasting models that use chronologically arranged data to develop forecasts.
• Assume that what happened in the past is a good starting point for predicting what will happen in the future.
• These models can be designed to account for:
  • Randomness
  • Trend
• Seasonality effects
• Advantages
• Can quickly be applied to a large number of products

• Forecast accuracy measures can be used to identify forecasts that need adjustment (management by exception

h2. Time Series Components of Demand…

Randomness

Randomness & trend

Randomness, trend & seasonality

h2. Basic Idea Behind Time Series Models

…Distinguish between random fluctuations & true changes in underlying demand patterns.

Simplicity is a virtue – Choose the simplest model that does the job

h2. Moving Average Models

• Based on last x periods
• Smoothes out random fluctuations

• Different weights can be applied to past observations, if desired

h2. Table of Forecasts & Demand Values…

h2. … and Resulting Graph

Note how the forecasts smooth out variations.