Mapa mental
Packet 10 Concept Map

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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
- Construction of time series
- 1. E(Yt). 2. Rt 3. Yt = E(Yt) + Rt
- If evidence of autocorrelation, use a model with...
- 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
- Accuracy
- MAPE (mean absolute percentage error)
- MAD (mean absolute deviation
- RMSE (root mean squared error)
- Regression Approach
- Multilinear regression model with no cyclical component
- Forecasting with time series autoregressive models
- Forecasting limits
- One step
- Two Step
- M 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
- Autoregressive error
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Información
- Descripción
- Olivia Barnard Kaleigh Brauns Carolyn Davin Sarah Herber
- Creado por
- Kaleigh Brauns
- Visitas
- 82
- Valoración