ARIMA Modeling
Build and tune ARIMA models for time series forecasting with proper parameter selection and diagnostics.
3
ARIMA Modeling
python • Step 3
Build and tune ARIMA models for time series forecasting with proper parameter selection and diagnostics.
arimastatsmodelsmodel_selectionforecasting
Learning Objectives
- →Fit ARIMA models with selected (p,d,q) parameters
- →Use a small grid search to pick a good (p,d,q) order
- →Diagnose model residuals
- →Generate forecasts with confidence intervals
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
datevalue- 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:
AIC: BIC:
0/3
Export to GitHub
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