17913083
Descrição
Mapa Mental por Kaleigh Brauns, atualizado more than 1 year ago
|
Criado por Kaleigh Brauns
quase 5 anos atrás
|
|
Packet 10 Concept Map
- Time Series
- Goal: Generalize the linear
regression model to account for
dependence of observation
- ex: consumer price index,
temperatures, product
consumption
- Secular trend, cyclical fluctuation,
seasonal variation, residual effect
- ex: consumer price index,
temperatures, product
consumption
- Construction of
time series
- 1. E(Yt). 2. Rt 3. Yt = E(Yt) + Rt
- If evidence of
autocorrelation,
use a model with...
- 1. E(Yt). 2. Rt 3. Yt = E(Yt) + Rt
- Goal: Generalize the linear
regression model to account for
dependence of observation
- Forecasting
- Smoothing Techniques
- Goal: Identify secular
trends apart from
random fluctuations
- Moving Averages
- Exponential Smoothing
- Goal: Identify secular
trends apart from
random fluctuations
- Accuracy
- MAPE (mean absolute percentage error)
- MAD (mean absolute deviation
- RMSE (root mean squared error)
- MAPE (mean absolute percentage error)
- Regression Approach
- Multilinear regression model with no cyclical component
- Multilinear regression model with no cyclical component
- Forecasting with
time series
autoregressive
models
- Forecasting limits
- One step
- Two Step
- M step
- One step
- Forecasting limits
- Smoothing Techniques
- Autocorrelation
- Goal: to correlate time series residuals at different points in time
- Properties
- Tends to be positive
- Diminishes over time
- First order autoregressive error model
- First order autoregressive error model
- Tends to be positive
- Autoregressive error
- Goal: to correlate time series residuals at different points in time
Anexos de mídia
Quer criar seus próprios Mapas Mentais gratuitos com a GoConqr? Saiba mais.