Analisis data - harga komoditas

Mencari keuntungan di komoditas tidaklah mudah. Sebagian besar trader komoditas justru merugi. Paket astsa menyertakan himpunan data chicken, yaitu harga spot bulanan untuk ayam utuh, Georgia docks, sen AS per pon, dari Agustus 2001 hingga Juli 2016.

Paket astsa sudah dimuat di konsol R Anda dan datanya telah diplot untuk Anda; perhatikan komponen tren dan musiman.

Pertama, Anda akan menggunakan keterampilan Anda untuk memasangkan model SARIMA secara cermat pada komoditas tersebut. Selanjutnya, Anda akan menggunakan model terpasang untuk mencoba memprakirakan harga spot ayam utuh.

Setelah menghilangkan tren, ACF dan PACF sampel menyarankan model AR(2) karena PACF terputus setelah lag 2 dan ACF menurun perlahan. Namun, ACF masih memiliki komponen musiman kecil yang tersisa. Hal ini dapat ditangani dengan menambahkan komponen SAR(1).

Omong-omong, jika Anda tertarik menganalisis komoditas lain dari berbagai wilayah, Anda dapat menemukan banyak deret waktu di index mundi.

Latihan ini adalah bagian dari kursus

Model ARIMA di R

Petunjuk latihan

Plot data terdiferensiasi (d = 1) diff(chicken). Perhatikan bahwa tren telah dihilangkan dan amati perilaku musiman.
Plot ACF dan PACF sampel dari data terdiferensiasi hingga lag 60 (5 tahun). Perhatikan bahwa AR(2) tampak sesuai, tetapi masih ada komponen musiman kecil namun signifikan yang tersisa pada data tanpa tren.
Pasangkan ARIMA(2,1,0) ke data chicken untuk melihat bahwa masih ada korelasi pada residual.
Pasangkan SARIMA(2,1,0)x(1,0,0)₁₂ dan perhatikan bahwa modelnya pas dengan baik.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Plot differenced chicken


# Plot P/ACF pair of differenced data to lag 60


# Fit ARIMA(2,1,0) to chicken - not so good


# Fit SARIMA(2,1,0,1,0,0,12) to chicken - that works

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Model ARIMA di R

SkillTag.level.beginnerSkillTag.label

4.9+

Mulai Kursus Gratis

You will investigate the nature of time series data and learn the basics of ARMA models that can explain the behavior of such data. You will learn the basic R commands needed to help set up raw time series data to a form that can be analyzed using ARMA models.

Exercise 1: First things first Exercise 2: Data play Exercise 3: Elements of time series Exercise 4: Stationarity and nonstationarity Exercise 5: Differencing Exercise 6: Detrending data Exercise 7: Dealing with trend and heteroscedasticity Exercise 8: Stationary time series: ARMA Exercise 9: Simulating ARMA models

You will discover the wonderful world of ARMA models and how to fit these models to time series data. You will learn how to identify a model, how to choose the correct model, and how to verify a model once you fit it to data. You will learn how to use R time series commands from the stats and astsa packages.

Exercise 1: AR and MA models Exercise 2: Fitting an AR(1) model Exercise 3: Fitting an AR(2) model Exercise 4: Fitting an MA(1) model Exercise 5: AR and MA together Exercise 6: Fitting an ARMA model Exercise 7: Identify an ARMA model Exercise 8: Model choice and residual analysis Exercise 9: Model choice - I Exercise 10: Model choice - II Exercise 11: Residual analysis - I Exercise 12: Residual analysis - II Exercise 13: ARMA get in

Now that you know how to fit ARMA models to stationary time series, you will learn about integrated ARMA (ARIMA) models for nonstationary time series. You will fit the models to real data using R time series commands from the stats and astsa packages.

Exercise 1: ARIMA - integrated ARMA Exercise 2: ARIMA - plug and play Exercise 3: Simulated ARIMA Exercise 4: Global warming Exercise 5: ARIMA diagnostics Exercise 6: Diagnostics - simulated overfitting Exercise 7: Diagnostics - global temperatures Exercise 8: Forecasting ARIMA Exercise 9: Forecasting simulated ARIMA Exercise 10: Forecasting global temperatures

You will learn how to fit and forecast seasonal time series data using seasonal ARIMA models. This is accomplished using what you learned in the previous chapters and by learning how to extend the R time series commands available in the stats and astsa packages.

Exercise 1: Model musiman murni Exercise 2: P/ACF untuk model musiman murni Exercise 3: Paskan model murni musiman Exercise 4: Model musiman campuran Exercise 5: Sesuaikan model musiman campuran Exercise 6: Analisis data - pengangguran I Exercise 7: Analisis data - pengangguran II Exercise 8: Analisis data - harga komoditas

Latihan Saat Ini

Exercise 9: Analisis data - tingkat kelahiran Exercise 10: Peramalan ARIMA musiman Exercise 11: Peramalan pengangguran bulanan Exercise 12: Seberapa sulit memproyeksikan harga komoditas?Exercise 13: Selamat!