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.

First things first

Data play

Elements of time series

Stationarity and nonstationarity

Differencing

Detrending data

Dealing with trend and heteroscedasticity

Stationary time series: ARMA

Simulating ARMA models

Time Series Data and 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.

AR and MA models

Fitting an AR(1) model

Fitting an AR(2) model

Fitting an MA(1) model

AR and MA together

Fitting an ARMA model

Identify an ARMA model

Model choice and residual analysis

Model choice - I

Model choice - II

Residual analysis - I

Residual analysis - II

ARMA get in

Fitting ARMA models

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.

ARIMA - integrated ARMA

ARIMA - plug and play

Simulated ARIMA

Global warming

ARIMA diagnostics

Diagnostics - simulated overfitting

Diagnostics - global temperatures

Forecasting ARIMA

Forecasting simulated ARIMA

Forecasting global temperatures

ARIMA Models

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.

Model musiman murni

P/ACF untuk model musiman murni

Paskan model murni musiman

Model musiman campuran

Sesuaikan model musiman campuran

Analisis data - pengangguran I

Analisis data - pengangguran II

Analisis data - harga komoditas

Analisis data - tingkat kelahiran

Peramalan ARIMA musiman

Peramalan pengangguran bulanan

Seberapa sulit memproyeksikan harga komoditas?

Selamat!

Seasonal ARIMA

Dalam kursus ini, Anda akan menjadi ahli dalam melakukan fitting model ARIMA pada data runtun waktu menggunakan R. Pertama, Anda akan menelusuri karakteristik data runtun waktu dengan alat pada paket R stats. Selanjutnya, Anda mempelajari cara melakukan fitting berbagai model ARMA pada data tersimulasi (di mana Anda mengetahui model yang benar) menggunakan paket R astsa. Setelah menguasai dasar-dasarnya, Anda akan mempelajari cara melakukan fitting model ARMA terintegrasi, atau model ARIMA, pada berbagai himpunan data nyata. Anda akan mempelajari cara memeriksa validitas model ARIMA dan cara melakukan peramalan data runtun waktu. Terakhir, Anda akan mempelajari cara melakukan fitting model ARIMA untuk data musiman, termasuk peramalan menggunakan paket astsa.

Time Series Analysis in R

Dalam kursus ini, Anda akan ahli dalam memodelkan ARIMA pada data deret waktu menggunakan R.

Model ARIMA di R

Jadilah ahli dalam menerapkan model ARIMA (Autoregressive Integrated Moving Average) pada data deret waktu menggunakan R.

Analis Kuantitatif  di R

Deret Waktu  di R

Peramalan pengangguran bulanan

Model ARIMA di R

Petunjuk latihan

Latihan interaktif praktis