Previsão bottom-up para o estado inteiro

Agora que você tem previsões para cada região, pode usar a hierarquia bottom-up para calcular a previsão para o estado inteiro! Suas previsões regionais estão salvas como for_SEC_t_xts, for_MET_t_xts e for_M_t_xts.

Este exercicio faz parte do curso

Previsão de Demanda de Produtos em R

Instruções do exercicio

Some todas as suas previsões regionais em uma variável chamada for_state para prever o estado inteiro!

exercicio interativo prático

Tente este exercicio completando este código de exemplo.

# Calculate the state sales forecast: for_state
for_state <- ___ + ___ + ___

# See the forecasts
for_state

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Este exercicio faz parte do curso

Previsão de Demanda de Produtos em R

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When it comes to forecasting, time series modeling is a great place to start! You need to forecast out the future values of sales demand and a good baseline approach would be ARIMA models. In this chapter you'll learn how to quickly implement ARIMA models and get good initial forecasts for future product demand.

Exercise 1: Loading data into xts object Exercise 2: Importing data Exercise 3: Plotting / visualizing data Exercise 4: ARIMA Time Series 101 Exercise 5: auto.arima() function Exercise 6: Interpret auto.arima Exercise 7: Forecasting and Evaluating Exercise 8: Forecast function Exercise 9: Calculating MAPE and MAE Exercise 10: Visualizing Forecast Exercise 11: Confidence Intervals for Forecast

Economic theory has a lot to say about predicting values of demand. Obviously, external factors like price, seasonality, and timing of promotions will drive some aspects of product demand. In this chapter you'll learn about the basics around price elasticity models and how to incorporate seasonality and promotion timing factors into our product demand forecasts.

Exercise 1: Price elasticity Exercise 2: Calculating price elasticity Exercise 3: Interpret results from elasticity Exercise 4: Seasonal / holiday / promotional effects Exercise 5: Visualize holiday / promotion effects Exercise 6: Create holiday / promotional effect variables Exercise 7: Regression for holiday / promotional effects Exercise 8: Significant holiday or promotional effects?Exercise 9: Forecasting with regression Exercise 10: Create future predictor variables Exercise 11: Forecast future values of demand Exercise 12: Visualizing forecasts of regression

Time series models and pricing regressions don't have to be thought of as separate approaches to product demand forecasting. They can be combined! In this chapter you'll learn about two ways of "combining" the information gained in both modeling approaches - transfer functions and forecast ensembling.

Exercise 1: Residuals from regression model Exercise 2: Calculating residuals from regression Exercise 3: Visualizing residuals Exercise 4: Forecasting residuals Exercise 5: auto.arima function Exercise 6: Forecasting residuals with time series Exercise 7: Visualizing residual forecasts Exercise 8: Transfer functions & ensembling Exercise 9: Combining residuals from regression & time series Exercise 10: Visualizing transfer function forecast Exercise 11: Calculating transfer function MAPE and MAE Exercise 12: ARIMA Forecasting Exercise 13: Ensembling of Forecasts

Everything up until this point deals with making individual models for forecasting product demand. However, we haven't taken advantage of the fact that all of these products form a product hierarchy of sales. Products make up regions and regions make up states. How can we ensure that our forecasts reconcile correctly up and down the hierarchy? In this chapter you'll learn about hierarchical forecasting and how to use it to your advantage in forecasting product demand.

Exercise 1: Previsão hierárquica de baixo para cima Exercise 2: Construir previsão de série temporal para novo produto Exercise 3: Crie uma previsão por regressão para um novo produto Exercise 4: Previsão em ensemble para novo produto Exercise 5: Calcular previsão bottom-up para toda a região Exercise 6: Previsão Hierárquica Top-Down Exercise 7: Criar previsão de série temporal no nível regional Exercise 8: Usando proporções históricas médias Exercise 9: Usando a proporção de médias históricas Exercise 10: Previsão Hierárquica Middle-Out Exercise 11: Criar previsão de série temporal para nova região Exercise 12: Previsão top-down para nova região Exercise 13: Previsão bottom-up para o estado inteiro

Exercicio Atual

Exercise 14: Parabéns!