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In the two graphs below (left and right), blue lines are sales and red lines are forecast. Which forecast has a relatively smaller smoothing constant α?

Forecast bias can be potentially addressed by _______.

In Joe Henwood’s presentation, many features (weather, store locations, etc.) were incorporated into the forecasting model. Including as many features as possible in a forecasting model always leads to better predictions. The statement is

For simple exponential smoothing with small smoothing constant α, which of the following statements are correct?

The following table shows actual sales and two forecasts for the past two periods. Compare the forecast errors between Forecast 1 and Forecast 2, which forecast has a larger bias?

When applying simple exponential smoothing, simple moving average, or weighted moving average methods, we assume that no trend is present. If a clear trend exists, these models are likely to produce underestimates or overestimates.

You’ve made a forecast using a simple exponential smoothing model based on past sales from 100 periods. By minimizing rMSE between your forecast and actual sales data, you can find the optimal value for alpha.  Suppose there is a large shock to your actual sales and the shock happened in period 50: how would you anticipate this affecting the newly optimal value of alpha?

What does the term ‘singularity’ refer to in the context of AI?

With improved machinery and production practices, farmers can now use less energy in production. How this may affect the market equilibrium for agricultural products?​

Which one below is correct? Capture3.PNG