Business forecasting 101
Forecasting Methods, Models, Techniques
Determining the optimal forecast method is a rather complex science, especially across a large product line. This may be nearly impossible using only spreadsheets. However, sophisticated forecasting software can within seconds test multiple methods for each item to determine which method will give you the most accurate results.
Exponential smoothing is a more advanced form of time series forecasting. Unlike moving averages, exponential smoothing methods can capture trends and recurring patterns. They accomplish this by:
Emphasizing the more recent data (as opposed to a moving average which weights all data equally), and
Smoothing out fluctuations, which are often caused by pure randomness in the data (or “noise” in the system)
Forecasters determine the forecast weights, controlling how fast or slow the model responds to demand changes in your actuals. Not all exponential smoothing methods can handle seasonality or other recurring patterns.
Exponential smoothing forecasting methods include:
- Simple exponential smoothing
- Holt’s linear method
- Winters’ multiplicative season
- Winters’ additive season
- Croston’s intermittent demand
- ARIMA (Box-Jenkins)
Regression Analysis Models
Many companies use regression models to determine the relationship between demand and demand drivers. They are especially useful for seeing trends and seasonality.
Regression analysis methods include:
- Linear regression
- Hyberbolic trend
- Logarithmic trend
- Square root trend
- Quadratic trend
- Power trend
- Exponential trend
- Seasonal trend
Hybrid Forecasting Methods
Hybrid forecasting methods combine regression, data smoothing, and other techniques to produce forecasts that can compensate for the weaknesses of individual methods. For example, some forecasting methods are great at short-term forecasting, but cannot capture seasonality.
Hybrid forecasting methods include:
- Vanguard Dampened Trend – a powerful hybrid model that simultaneously detects all trends, cycles, and seasonality in historical data and responds with the most accurate exponential smoothing method. Vanguard Dampened Trend is available across all Vanguard business forecasting applications.
- Log Theta
Decomposition Forecasting Methods
Spectral-analysis forecasting models are effective at filtering noise out of cyclical data. For example, spectral analysis can pull seasonal patterns out from data that also have a strong monthly pattern. The seasonality would be otherwise difficult to see.
Spectral analysis forecasting methods include:
- Lowpass filter
- Spectral noise filter
- Formant frequency
Custom Forecasting Models
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