Analysis of Sequential Data
MSE Module TSM_AnSeqDa
MSE Module TSM_AnSeqDa
Fichier Détails
| Cartes-fiches | 96 |
|---|---|
| Langue | Deutsch |
| Catégorie | Mathématiques |
| Niveau | Université |
| Crée / Actualisé | 17.01.2021 / 08.09.2023 |
| Attribution de licence | Non précisé |
| Lien de web |
https://card2brain.ch/box/20210117_tsmanseqda
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| Intégrer |
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What can we forecast?
- Sells of pills/medicine
- Elextricity demand and availability
- Weather
- Sells of product/service
- Customer churns
Which factors affect forecastability?
- Some thing is easier to forecast if:
- we have a good understanding of the factors that contribute to it
- There is lots of data available
- the forecasts cannot affect the thing we are trying to forecast
- There is relatively low natural/unexplainable random variation
- the future is somewhat similar to the past
What are time series data?
- Daily stock prices
- Monthly rainfall
- Annual business profits
- Production, e.g. quartely australien beer production
What is forecasting about?
Forecasting is estimating how the sequence of observations will continue into the future
What do wee need to add to the forecast?
An uncerainty range
Why is providing an uncertainty of the forecast in forecasting important?
If you have just the probabilty (e.g. 50%) you do not know the deviation. So you could for example produce the 80% of the forecast. The worst case scenario is that customer could order or demand more. If you can not provide the asked product of piece, you have the bigger loss (in image) than if you have produced a bit too much, which you can hold and put in the sale in the end.
When is it ok to use the gaussian distribution in forecasting?
As long as the to be predicted number is far away from 0 (like hundreds of thousends of better millions)
Name the definitions of statistical forecasting:
