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:
- Seasonality effects
- 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
Randomness & trend
Randomness, trend & seasonality
…Distinguish between random fluctuations & true changes in underlying demand patterns.
Simplicity is a virtue – Choose the simplest model that does the job
- Based on last x periods
- Smoothes out random fluctuations
- Different weights can be applied to past observations, if desired
Note how the forecasts smooth out variations.