Time Series Fundamentals
Master autocorrelation analysis, differencing for stationarity, and rolling statistics to prepare data for forecasting models.
2
Time Series Fundamentals
python • Step 2
Master autocorrelation analysis, differencing for stationarity, and rolling statistics to prepare data for forecasting models.
time_seriesautocorrelationdifferencingrolling_stats
Learning Objectives
- →Compute and interpret ACF and PACF plots
- →Apply differencing to achieve stationarity
- →Calculate rolling mean and standard deviation
- →Determine ARIMA (p, d, q) parameters from ACF/PACF
Dataset
View on Kaggle Matches Kaggle
Superstore Sales dataset — Order Date, Sales, Category, Region, etc.
*bleep* Load the dataset in the browser terminal:
from pyodide.http import open_url
import pandas as pd
df = pd.read_csv(open_url("/datasets/train.csv"))
print(df.shape)
print(df.columns.tolist()) Running locally? The file inside the Kaggle download is already named train.csv — drop it into the notebook's working directory and the code above runs unchanged (swap open_url(...) for "train.csv").
Dataset Columns
datevaluediff_1diff_seasonal- Write your solution in the Try It Yourself editor below — matplotlib/seaborn figures and pandas tables render inline.
- Stuck? Expand a section and click ↑ Send to editor to stack its snippet into the editor, notebook-style.
- Hit ▶ Run (or ⌘↵) — once your output matches the expected result, the step is marked solved.
- Use ↺ Reset to restore the starter code at any time.
Try It Yourself
Python
Loading editor...
*bleep* Output will appear here after running your code. Figures from matplotlib/seaborn render inline.
Expected output:
After differencing Stationary:
0/3
Export to GitHub
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