Menghitung MAPE dan MAE fungsi transfer

Seberapa melesetkah peramalan ini? MAE dan MAPE akan membantu kita memahaminya. Data validasi Anda disimpan sebagai MET_hi_v dan peramalan final Anda adalah for_MET_hi_final. Ingat bahwa MAE adalah selisih absolut, sedangkan MAPE adalah selisih absolut relatif.

Latihan ini adalah bagian dari kursus

Peramalan Permintaan Produk dengan R

Petunjuk latihan

Hitung MAE dari peramalan ini.
Hitung MAPE dari peramalan ini.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Calculate the MAE
MAE <- mean(abs(___ - ___))
print(MAE)

# Calculate the MAPE
MAPE <- 100*mean(abs((___ - ___)/___))
print(MAPE)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Peramalan Permintaan Produk dengan R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Residual dari model regresi Exercise 2: Menghitung residual dari regresi Exercise 3: Memvisualisasikan residual Exercise 4: Meramalkan residual Exercise 5: Fungsi auto.arima Exercise 6: Peramalan residual dengan deret waktu Exercise 7: Memvisualisasikan peramalan residual Exercise 8: Fungsi transfer & ensembling Exercise 9: Menggabungkan residual dari regresi & deret waktu Exercise 10: Memvisualisasikan peramalan fungsi transfer Exercise 11: Menghitung MAPE dan MAE fungsi transfer

Latihan Saat Ini

Exercise 12: Peramalan ARIMA Exercise 13: Ensembling Ramalan

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: Bottom-Up Hierarchical Forecasting Exercise 2: Build time series forecast for new product Exercise 3: Build regression forecast for new product Exercise 4: Ensemble forecast for new product Exercise 5: Calculate bottom-up forecast for whole region Exercise 6: Top-Down Hierarchical Forecasting Exercise 7: Build time series forecast at regional level Exercise 8: Using average historical proportions Exercise 9: Using proportion of historical averages Exercise 10: Middle-Out Hierarchical Forecasting Exercise 11: Build time series forecast for new region Exercise 12: Top-down forecast for new region Exercise 13: Bottom-up forecast for whole state Exercise 14: Congratulations!