re-brand to graded intervention time series

Renamed 'tfits_single_channel.ipynb' to 'graded_intervention_time_series_single_channel_ols.ipynb' and updated the notebook title and references in both the notebook and the index.md file to reflect the new name and description.

Author: drbenvincent

…rce/notebooks/tfits_single_channel.ipynb …ion_time_series_single_channel_ols.ipynbdocs/source/notebooks/tfits_single_channel.ipynb renamed to docs/source/notebooks/graded_intervention_time_series_single_channel_ols.ipynb docs/source/notebooks/tfits_single_channel.ipynb renamed to docs/source/notebooks/graded_intervention_time_series_single_channel_ols.ipynb

@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Transfer Function Interrupted Time Series (TF-ITS)\n",
+    "# Graded Intervention Time Series (OLS)\n",
     "\n",
-    "**Transfer Function Interrupted Time Series (TF-ITS)** extends classical interrupted time series analysis to handle **graded interventions** - policies or treatments with varying intensity over time, rather than simple on/off changes {cite:p}`box1975intervention`. Traditional ITS methods model binary interventions (e.g., \"policy enacted\" vs \"no policy\"). TF-ITS handles more realistic scenarios where:\n",
+    "**Graded Intervention Time Series** extends classical interrupted time series analysis to handle **graded interventions** - policies or treatments with varying intensity over time, rather than simple on/off changes. Traditional ITS methods model binary interventions (e.g., \"policy enacted\" vs \"no policy\"). This method (technically called Transfer Function Interrupted Time Series or TF-ITS in the literature {cite:p}`box1975intervention`) handles more realistic scenarios where:\n",
     "\n",
     "1. **Intervention intensity varies continuously** (e.g., advertising spend \\$0 - 100k, communication frequency 0-10 messages/week)\n",
     "2. **Effects saturate** - diminishing returns as exposure increases (10th message less impactful than the 1st)\n",
@@ -20,9 +20,9 @@
     "- **{term}`Counterfactual` analysis**: Estimate effects by zeroing or scaling interventions\n",
     "- **HAC standard errors**: Robust inference accounting for autocorrelation and heteroskedasticity\n",
     "\n",
-    "### When to Use TF-ITS\n",
+    "### When to Use Graded Intervention Time Series\n",
     "\n",
-    "Use TF-ITS when you have:\n",
+    "Use this method when you have:\n",
     "- ✅ Time series data from a **single unit** (region, market, organization)\n",
     "- ✅ **Graded intervention** with varying intensity over time\n",
     "- ✅ Reason to expect **saturation** (diminishing returns) or **carryover effects** (persistence)\n",

docs/source/notebooks/index.md

@@ -46,7 +46,7 @@ its_covid.ipynb
 :caption: Graded Intervention Time Series
 :maxdepth: 1
 
-tfits_single_channel.ipynb
+graded_intervention_time_series_single_channel_ols.ipynb
 :::
 
 :::{toctree}