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Module 1: Módulo 2: Introdução a Métodos de Previsão

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Time Series Models:
Assumes information needed to generate a forecast is contained in a time series of data
Assumes the future will follow same patterns as the past



Forecaster looks for data patterns as
Data = historic pattern + random variation
Historic pattern to be forecasted:

Level (long-term average) – data fluctuates around a constant mean
Trend – data exhibits an increasing or decreasing pattern
Seasonality – any pattern that regularly repeats itself and is of a constant length
Cycle – patterns created by economic fluctuations
Random Variation cannot be predicted


Simple Mean:
The average of all available data - good for level patterns

Moving Average:
The average value over a set time period (e.g.: the last four weeks)
Each new forecast drops the oldest data point & adds a new observation
More responsive to a trend but still lags behind actual data


Weighted Moving Average:
All weights must add to 100% or 1.00
e.g. Ct .5, Ct-1 .3, Ct-2 .2 (weights add to 1.0)

Allows emphasizing one period over others; above indicates more weight on recent data (Ct=.5)
Differs from the simple moving average that weighs all periods equally - more responsive to trends

Exponential Smoothing:
Most frequently used time series method because of ease of use and minimal amount of data needed


Need just three pieces of data to start:
Last period’s forecast (Ft)
Last periods actual value (At)
Select value of smoothing coefficient, ,between 0 and 1.0

If no last period forecast is available, average the last few periods