Membuat prediksi satu-langkah-ke-depan

Memprediksi harga saham sangatlah sulit. Ekonomi klasik bahkan menyatakan bahwa hal ini seharusnya mustahil karena mekanisme keseimbangan pasar.

Tugas Anda pada latihan ini adalah mencoba yang tampaknya mustahil: memprediksi harga saham Amazon.

Di latihan ini Anda akan membuat prediksi satu-langkah-ke-depan untuk harga saham sekaligus ketidakpastian dari prediksi tersebut.

Sebuah model telah dipasangkan ke data Amazon untuk Anda. Objek hasil dari model ini tersedia di lingkungan Anda sebagai results.

Latihan ini adalah bagian dari kursus

Model ARIMA di Python

Petunjuk latihan

Gunakan objek results untuk membuat prediksi satu-langkah-ke-depan pada 30 hari data terbaru dan simpan hasilnya ke one_step_forecast.
Simpan prediksi rata-rata Anda ke mean_forecast menggunakan salah satu atribut dari objek one_step_forecast.
Ekstrak selang kepercayaan dari prediksi Anda dari objek one_step_forecast dan simpan ke confidence_intervals.
Cetak prediksi rata-rata Anda.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Generate predictions
one_step_forecast = results.____(____=___)

# Extract prediction mean
mean_forecast = one_step_forecast.____

# Get confidence intervals of  predictions
confidence_intervals = one_step_forecast.____

# Select lower and upper confidence limits
lower_limits = confidence_intervals.loc[:,'lower close']
upper_limits = confidence_intervals.loc[:,'upper close']

# Print best estimate  predictions
print(____)

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

Latihan Saat Ini

Exercise 7: Memplot prediksi satu-langkah-ke-depan Exercise 8: Membuat peramalan dinamis Exercise 9: Memplot peramalan dinamis 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!