# Deret Waktu & Peramalan (Data Science & ML)

> Analisis waktu, stasioneritas, ARIMA, Prophet, seasonal decomposition, metrik peramalan, backtesting

- 22 pertanyaan wawancara
- Mid-Level
- [Pertanyaan Wawancara: Data Science & ML](https://sharpskill.dev/id/technologies/data-science/pertanyaan-wawancara.md)

## 1. Apa itu time series?

**Jawaban**

Time series adalah urutan titik data yang diindeks dalam urutan kronologis. Observasi dikumpulkan pada interval reguler (per jam, harian, bulanan) dan sering menunjukkan dependensi temporal. Contoh klasik termasuk harga saham, suhu, dan penjualan bulanan.

## 2. Apa tiga komponen utama dari time series dalam dekomposisi klasik?

**Jawaban**

Dekomposisi klasik time series mengidentifikasi tiga komponen: trend (evolusi jangka panjang), seasonality (pola berulang pada interval tetap), dan residual (noise acak yang tidak dijelaskan). Dekomposisi ini dapat aditif atau multiplikatif tergantung pada sifat data.

## 3. Apa itu stasioneritas dalam time series?

**Jawaban**

Time series bersifat stasioner ketika properti statistiknya (mean, variance, autocorrelation) tetap konstan dari waktu ke waktu. Stasioneritas adalah asumsi fundamental untuk banyak model peramalan seperti ARIMA. Time series non-stasioner sering perlu ditransformasi (differencing) sebelum pemodelan.

## 19 pertanyaan lagi tersedia

- Uji statistik mana yang umum digunakan untuk memeriksa stasioneritas time series?
- Bagaimana cara membuat time series non-stasioner menjadi stasioner?

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