Memplot peramalan dinamis

Saatnya memplot prediksi Anda. Ingat bahwa membuat prediksi dinamis berarti model Anda membuat prediksi tanpa koreksi, berbeda dengan prediksi satu-langkah-ke-depan. Ini mirip dengan membuat peramalan sekarang untuk 30 hari ke depan, lalu menunggu untuk melihat apa yang terjadi sebelum membandingkan seberapa baik prediksi Anda.

DataFrame lower_limits, upper_limits, dan amazon, serta prediksi rata-rata Anda mean_forecast yang Anda buat pada latihan sebelumnya tersedia di lingkungan Anda.

Latihan ini adalah bagian dari kursus

Model ARIMA di Python

Petunjuk latihan

Plot data amazon dengan menggunakan tanggal pada indeks DataFrame ini sebagai koordinat x dan nilainya sebagai koordinat y.
Plot prediksi mean_forecast dengan cara yang sama.
Plot area berarsir antara lower_limits dan upper_limits dari interval kepercayaan Anda. Gunakan indeks dari salah satu DataFrame tersebut sebagai koordinat x.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# plot the amazon data
plt.plot(____, ____, label='observed')

# plot your mean forecast
plt.plot(____, ____, color='r', label='forecast')

# shade the area between your confidence limits
plt.____(____, ____, 
         ____, color='pink')

# set labels, legends and show plot
plt.xlabel('Date')
plt.ylabel('Amazon Stock Price - Close USD')
plt.legend()
plt.show()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Model ARIMA di Python

SkillTag.level.advancedSkillTag.label

4.9+

Mulai Kursus Gratis

Dive straight in and learn about the most important properties of time series. You'll learn about stationarity and how this is important for ARMA models. You'll learn how to test for stationarity by eye and with a standard statistical test. Finally, you'll learn the basic structure of ARMA models and use this to generate some ARMA data and fit an ARMA model.

Exercise 1: Intro to time series and stationarity Exercise 2: Exploration Exercise 3: Train-test splits Exercise 4: Is it stationary Exercise 5: Making time series stationary Exercise 6: Augmented Dicky-Fuller Exercise 7: Taking the difference Exercise 8: Other tranforms Exercise 9: Intro to AR, MA and ARMA models Exercise 10: Model order Exercise 11: Generating ARMA data Exercise 12: Fitting Prelude

What lies ahead in this chapter is you predicting what lies ahead in your data. You'll learn how to use the elegant statsmodels package to fit ARMA, ARIMA and ARMAX models. Then you'll use your models to predict the uncertain future of stock prices!

Exercise 1: Menerapkan model deret waktu Exercise 2: Mencocokkan model AR dan MA Exercise 3: Mengesuaikan model ARMA Exercise 4: Menerapkan model ARMAX Exercise 5: Peramalan Exercise 6: Membuat prediksi satu-langkah-ke-depan Exercise 7: Memplot prediksi satu-langkah-ke-depan Exercise 8: Membuat peramalan dinamis Exercise 9: Memplot peramalan dinamis

Latihan Saat Ini

Exercise 10: Pengantar model ARIMA Exercise 11: Pendiferensian dan pemfittingan ARMA Exercise 12: Membuka gulungan prakiraan ARMA Exercise 13: Memasang model ARIMA Exercise 14: Memilih model ARIMA

In this chapter, you will become a modeler of discerning taste. You'll learn how to identify promising model orders from the data itself, then, once the most promising models have been trained, you'll learn how to choose the best model from this fitted selection. You'll also learn a great framework for structuring your time series projects.

Exercise 1: Intro to ACF and PACF Exercise 2: AR or MA Exercise 3: Order of earthquakes Exercise 4: Intro to AIC and BIC Exercise 5: Searching over model order Exercise 6: Choosing order with AIC and BIC Exercise 7: AIC and BIC vs ACF and PACF Exercise 8: Model diagnostics Exercise 9: Mean absolute error Exercise 10: Diagnostic summary statistics Exercise 11: Plot diagnostics Exercise 12: Box-Jenkins method Exercise 13: Identification Exercise 14: Identification II Exercise 15: Estimation Exercise 16: Diagnostics

In this final chapter, you'll learn how to use seasonal ARIMA models to fit more complex data. You'll learn how to decompose this data into seasonal and non-seasonal parts and then you'll get the chance to utilize all your ARIMA tools on one last global forecast challenge.

Exercise 1: Seasonal time series Exercise 2: Seasonal decompose Exercise 3: Seasonal ACF and PACF Exercise 4: SARIMA models Exercise 5: Fitting SARIMA models Exercise 6: Choosing SARIMA order Exercise 7: SARIMA vs ARIMA forecasts Exercise 8: Automation and saving Exercise 9: Automated model selection Exercise 10: Saving and updating models Exercise 11: SARIMA and Box-Jenkins Exercise 12: Multiplicative vs additive seasonality Exercise 13: SARIMA model diagnostics Exercise 14: SARIMA forecast Exercise 15: Congratulations!