Getting Started with R#

Stage 1 · 🧭 Orientation · Lesson 02 of 18 · beginner

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Important

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The toolkit#

The source course is taught in R, the classic Box–Jenkins environment. This reference reframes the same ideas in Python, using the standard scientific stack: NumPy for arrays, pandas for time-indexed data, statsmodels for the models (AR / ARMA / ARIMA / SARIMAX, the ACF / PACF, and the ADF / KPSS stationarity tests), and Matplotlib for plots. The mathematics is identical in either language; only the syntax differs.

Loading a series#

In Python a time series is a pandas.Series (or a DataFrame column) carrying a DatetimeIndex, so pandas knows the spacing and can resample, align and difference for you:

import pandas as pd

s = pd.read_csv("sales.csv", parse_dates=["date"], index_col="date")["value"]
s = s.asfreq("MS")          # pin an explicit monthly-start frequency
s.plot(title="Monthly sales")

Setting an explicit frequency (asfreq) matters: many models need to know the season length.

From R to Python#

The classic R verbs map cleanly onto the Python stack:

ts() → a pandas.Series with a DatetimeIndex;
acf() / pacf() → statsmodels.graphics.tsaplots.plot_acf / plot_pacf;
arima() / Arima() → statsmodels.tsa.arima.model.ARIMA;
forecast() → results.get_forecast(steps=...);
auto.arima() → pmdarima.auto_arima.

Reach for whichever environment you like; this course uses the Python calls throughout.

Hint