{% if sections.acf_pacf is defined %}

📊 ACF & PACF Analysis

The ACF and PACF tables present the numerical values of autocorrelation and partial autocorrelation coefficients for each variable across specified lags. These tabulated values enable precise identification of statistically significant lags and the strength of relationships over time.

{% set columns = sections.acf_pacf.data %} {% set images = sections.acf_pacf.chart.images %}
{% set max_lags = columns | map(attribute='lags') | map('length') | max %} {% for lag in range(0, max_lags) %} {% endfor %} {% for col in columns %} {% for val in col.acf %} {% endfor %} {% for _ in range(max_lags - (col.acf|length)) %} {% endfor %} {% for val in col.pacf %} {% endfor %} {% for _ in range(max_lags - (col.pacf|length)) %} {% endfor %} {% endfor %}
Lag
Columns Type{{ lag }}
{{ col.column }} ACF{{ "%.2f"|format(val) if val is number else val }}-
PACF{{ "%.2f"|format(val) if val is number else val }}-
{% set img = images[col.column] %} {% if img is defined %} Chart for {{ col.column }} {% else %} IMAGE place holder {% endif %}
{% endif %}