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   "source": [
    "# Cheat sheet: pick your bootstrap\n",
    "\n",
    "A scannable reference for choosing a `tsbootstrap` method. The capability\n",
    "table below is built from the library's own metadata, so it cannot drift\n",
    "from the code. The decision tree summarizes when each family applies.\n",
    "\n",
    "For the programmatic version of this table, see `diagnose`, which inspects\n",
    "your data and recommends methods. For the worked rationale behind each\n",
    "choice, see the which-bootstrap-when tutorial."
   ]
  },
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    "# On Colab or Binder, install tsbootstrap first (skipped if already present):\n",
    "try:\n",
    "    import tsbootstrap  # noqa: F401\n",
    "except ImportError:\n",
    "    %pip install -q \"tsbootstrap[examples]\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc712a98",
   "metadata": {},
   "source": [
    "## The capability table\n",
    "\n",
    "One row per method, read straight from `tsbootstrap.metadata.METHODS` and the\n",
    "spec classes in `tsbootstrap.methods`. The block-length column reports the\n",
    "actual parameter name on each spec (most use `block_length`; the stationary\n",
    "bootstrap uses `avg_block_length`; model-based methods have none)."
   ]
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>spec</th>\n",
       "      <th>serial dependence</th>\n",
       "      <th>marginal</th>\n",
       "      <th>block-length param</th>\n",
       "      <th>wraps around</th>\n",
       "      <th>multivariate</th>\n",
       "      <th>OOB</th>\n",
       "      <th>recommended for</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>iid</th>\n",
       "      <td>IID</td>\n",
       "      <td>no</td>\n",
       "      <td>empirical</td>\n",
       "      <td>none</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>i.i.d. data with no serial dependence; baseline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moving_block</th>\n",
       "      <td>MovingBlock</td>\n",
       "      <td>yes</td>\n",
       "      <td>empirical</td>\n",
       "      <td>block_length</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>stationary, weakly dependent series; distribut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>circular_block</th>\n",
       "      <td>CircularBlock</td>\n",
       "      <td>yes</td>\n",
       "      <td>empirical</td>\n",
       "      <td>block_length</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>stationary, weakly dependent series; distribut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stationary_block</th>\n",
       "      <td>StationaryBlock</td>\n",
       "      <td>yes</td>\n",
       "      <td>empirical</td>\n",
       "      <td>avg_block_length</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>stationary, weakly dependent series; distribut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_overlapping_block</th>\n",
       "      <td>NonOverlappingBlock</td>\n",
       "      <td>yes</td>\n",
       "      <td>empirical</td>\n",
       "      <td>block_length</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>stationary, weakly dependent series; distribut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tapered_block</th>\n",
       "      <td>TaperedBlock</td>\n",
       "      <td>yes</td>\n",
       "      <td>empirical</td>\n",
       "      <td>block_length</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>stationary, weakly dependent series; distribut...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residual</th>\n",
       "      <td>ResidualBootstrap</td>\n",
       "      <td>yes</td>\n",
       "      <td>model-implied</td>\n",
       "      <td>none</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>series well described by a fitted AR/ARIMA/VAR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sieve_ar</th>\n",
       "      <td>SieveAR</td>\n",
       "      <td>yes</td>\n",
       "      <td>model-implied</td>\n",
       "      <td>none</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>series well described by a fitted AR/ARIMA/VAR...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      spec serial dependence       marginal  \\\n",
       "method                                                                        \n",
       "iid                                    IID                no      empirical   \n",
       "moving_block                   MovingBlock               yes      empirical   \n",
       "circular_block               CircularBlock               yes      empirical   \n",
       "stationary_block           StationaryBlock               yes      empirical   \n",
       "non_overlapping_block  NonOverlappingBlock               yes      empirical   \n",
       "tapered_block                 TaperedBlock               yes      empirical   \n",
       "residual                 ResidualBootstrap               yes  model-implied   \n",
       "sieve_ar                           SieveAR               yes  model-implied   \n",
       "\n",
       "                      block-length param wraps around multivariate  OOB  \\\n",
       "method                                                                    \n",
       "iid                                 none           no          yes  yes   \n",
       "moving_block                block_length           no          yes  yes   \n",
       "circular_block              block_length          yes          yes  yes   \n",
       "stationary_block        avg_block_length           no          yes  yes   \n",
       "non_overlapping_block       block_length           no          yes  yes   \n",
       "tapered_block               block_length           no          yes  yes   \n",
       "residual                            none           no          yes   no   \n",
       "sieve_ar                            none           no           no   no   \n",
       "\n",
       "                                                         recommended for  \n",
       "method                                                                    \n",
       "iid                      i.i.d. data with no serial dependence; baseline  \n",
       "moving_block           stationary, weakly dependent series; distribut...  \n",
       "circular_block         stationary, weakly dependent series; distribut...  \n",
       "stationary_block       stationary, weakly dependent series; distribut...  \n",
       "non_overlapping_block  stationary, weakly dependent series; distribut...  \n",
       "tapered_block          stationary, weakly dependent series; distribut...  \n",
       "residual               series well described by a fitted AR/ARIMA/VAR...  \n",
       "sieve_ar               series well described by a fitted AR/ARIMA/VAR...  "
      ]
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   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from tsbootstrap.metadata import METHODS\n",
    "from tsbootstrap.methods import OBSERVATION_RESAMPLING\n",
    "\n",
    "\n",
    "def block_length_param(spec_type):\n",
    "    \"\"\"Return the block-length field name on a spec, or None if it has none.\"\"\"\n",
    "    fields = getattr(spec_type, \"model_fields\", {})\n",
    "    for name in fields:\n",
    "        if \"block_length\" in name:\n",
    "            return name\n",
    "    return None\n",
    "\n",
    "\n",
    "def recommended_for(meta, is_resampler):\n",
    "    \"\"\"Distill a one-line use case from assumptions and failure modes.\"\"\"\n",
    "    if not meta.preserves_temporal_dependence:\n",
    "        return \"i.i.d. data with no serial dependence; baseline\"\n",
    "    if is_resampler:\n",
    "        return \"stationary, weakly dependent series; distribution-free\"\n",
    "    return \"series well described by a fitted AR/ARIMA/VAR model\"\n",
    "\n",
    "\n",
    "rows = []\n",
    "for spec_type, meta in METHODS.items():\n",
    "    is_resampler = spec_type in OBSERVATION_RESAMPLING\n",
    "    # Resamplers reuse observed values, so they preserve the empirical marginal.\n",
    "    # Model-based methods regenerate from fitted dynamics, so their marginal is\n",
    "    # model-implied rather than empirical.\n",
    "    marginal = \"empirical\" if is_resampler else \"model-implied\"\n",
    "    rows.append(\n",
    "        {\n",
    "            \"method\": meta.name,\n",
    "            \"spec\": spec_type.__name__,\n",
    "            \"serial dependence\": \"yes\" if meta.preserves_temporal_dependence else \"no\",\n",
    "            \"marginal\": marginal,\n",
    "            \"block-length param\": block_length_param(spec_type) or \"none\",\n",
    "            \"wraps around\": \"yes\" if spec_type.__name__ == \"CircularBlock\" else \"no\",\n",
    "            \"multivariate\": \"yes\" if meta.supports_multivariate else \"no\",\n",
    "            \"OOB\": \"yes\" if meta.supports_oob else \"no\",\n",
    "            \"recommended for\": recommended_for(meta, is_resampler),\n",
    "        }\n",
    "    )\n",
    "\n",
    "table = pd.DataFrame(rows).set_index(\"method\")\n",
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5e1e6cd",
   "metadata": {},
   "source": [
    "Reading the columns:\n",
    "\n",
    "- **serial dependence**: whether the method preserves temporal structure.\n",
    "  Only `iid` does not.\n",
    "- **marginal**: block and i.i.d. methods resample observed values, so the\n",
    "  one-dimensional marginal is the empirical one. Model-based methods\n",
    "  regenerate data from fitted dynamics, so their marginal is whatever the\n",
    "  model implies, not the raw empirical distribution.\n",
    "- **block-length param**: the constructor argument controlling block size.\n",
    "  Pass `\"auto\"` to let the Politis-White rule pick it.\n",
    "- **wraps around**: only `CircularBlock` wraps blocks past the series end,\n",
    "  which removes the edge bias of plain moving blocks.\n",
    "- **OOB**: whether out-of-bag masks are defined, which the EnbPI\n",
    "  uncertainty layer needs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "212aed4b",
   "metadata": {},
   "source": [
    "## The decision tree\n",
    "\n",
    "The first split is whether the data has serial dependence. If not, use `IID`. If it does, choose between a block family\n",
    "(distribution-free, resamples observed values) and a model-based family\n",
    "(regenerates from a fitted model)."
   ]
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      "text/plain": [
       "<Figure size 1500x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import FancyArrowPatch, FancyBboxPatch\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 9))\n",
    "ax.set_xlim(0, 100)\n",
    "ax.set_ylim(0, 100)\n",
    "ax.axis(\"off\")\n",
    "\n",
    "\n",
    "def box(x, y, w, h, text, color):\n",
    "    \"\"\"Draw a rounded box centered text and return its center point.\"\"\"\n",
    "    patch = FancyBboxPatch(\n",
    "        (x - w / 2, y - h / 2),\n",
    "        w,\n",
    "        h,\n",
    "        boxstyle=\"round,pad=0.6,rounding_size=2\",\n",
    "        linewidth=1.2,\n",
    "        edgecolor=\"black\",\n",
    "        facecolor=color,\n",
    "    )\n",
    "    ax.add_patch(patch)\n",
    "    ax.text(x, y, text, ha=\"center\", va=\"center\", fontsize=9, wrap=True)\n",
    "    return x, y\n",
    "\n",
    "\n",
    "def arrow(p0, p1, label=None):\n",
    "    \"\"\"Draw an arrow from p0 to p1 with an optional mid-label.\"\"\"\n",
    "    ax.add_patch(\n",
    "        FancyArrowPatch(p0, p1, arrowstyle=\"-|>\", mutation_scale=14, linewidth=1.1, color=\"0.3\")\n",
    "    )\n",
    "    if label:\n",
    "        mx, my = (p0[0] + p1[0]) / 2, (p0[1] + p1[1]) / 2\n",
    "        ax.text(\n",
    "            mx,\n",
    "            my,\n",
    "            label,\n",
    "            ha=\"center\",\n",
    "            va=\"center\",\n",
    "            fontsize=8,\n",
    "            style=\"italic\",\n",
    "            color=\"0.2\",\n",
    "            bbox={\"boxstyle\": \"round,pad=0.2\", \"fc\": \"white\", \"ec\": \"none\"},\n",
    "        )\n",
    "\n",
    "\n",
    "blue, green, orange, gray = \"#cfe2f3\", \"#d9ead3\", \"#fce5cd\", \"#efefef\"\n",
    "\n",
    "# Top level: the first split.\n",
    "root = box(40, 93, 34, 8, \"Does the series have\\nserial dependence?\", gray)\n",
    "iid = box(13, 75, 22, 9, \"IID\\nplain i.i.d. resampling\\n(baseline only)\", blue)\n",
    "dep = box(58, 75, 32, 8, \"Yes. Resample values,\\nor model the dynamics?\", gray)\n",
    "\n",
    "# Second level: the two families, kept far apart on the x-axis.\n",
    "block = box(34, 58, 28, 8, \"Block family\\n(distribution-free)\", green)\n",
    "model = box(89, 58, 20, 8, \"Model-based\\n(parametric)\", orange)\n",
    "\n",
    "# Block family children: a top row of three and a bottom row of two placed in\n",
    "# the gaps below, all kept clear of the far-right model column.\n",
    "mb = box(11, 40, 22, 11, \"MovingBlock\\noverlapping blocks;\\nthe default\", green)\n",
    "cb = box(37, 40, 22, 11, \"CircularBlock\\nwrap-around blocks;\\nno edge bias\", green)\n",
    "tb = box(63, 40, 22, 11, \"TaperedBlock\\nwindow-weighted;\\nsofter block edges\", green)\n",
    "sb = box(24, 18, 22, 11, \"StationaryBlock\\nrandom block lengths;\\nblock-size robust\", green)\n",
    "nb = box(50, 18, 22, 11, \"NonOverlappingBlock\\ndisjoint blocks;\\nhigher variance\", green)\n",
    "\n",
    "# Model-based children, stacked in a dedicated far-right column (x = 89).\n",
    "res = box(89, 40, 22, 11, \"ResidualBootstrap\\nfit AR/ARIMA/VAR,\\nresample residuals\", orange)\n",
    "sv = box(89, 18, 22, 11, \"SieveAR\\nauto-order AR sieve\\n(univariate)\", orange)\n",
    "\n",
    "# Top-level arrows.\n",
    "arrow((33, 89), (17, 80), \"no\")\n",
    "arrow((47, 89), (54, 79), \"yes\")\n",
    "arrow((50, 71), (40, 62), \"values\")\n",
    "arrow((66, 71), (85, 62), \"dynamics\")\n",
    "\n",
    "# Block family to its children. The bottom-row connectors run straight down the\n",
    "# clear gap columns so they never cross a top-row box.\n",
    "arrow((28, 54), (12, 46))\n",
    "arrow((34, 54), (37, 46))\n",
    "arrow((40, 54), (62, 46))\n",
    "arrow((24, 53), (24, 24))\n",
    "arrow((50, 53), (50, 24))\n",
    "\n",
    "# Model-based family to its children (within the far-right lane).\n",
    "arrow((89, 54), (89, 46))\n",
    "arrow((89, 34), (89, 24))\n",
    "\n",
    "ax.set_title(\"Picking a tsbootstrap method\", fontsize=13, pad=12)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
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   "id": "ce52771f",
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   "source": [
    "## Where to go next\n",
    "\n",
    "This table and tree are the static reference. Two pointers for using them:\n",
    "\n",
    "- `diagnose` is the programmatic version of this table. It inspects your\n",
    "  series (serial dependence, stationarity) and returns recommended method\n",
    "  specifications. Treat it as a heuristic starting point, not a guarantee:\n",
    "\n",
    "  ```python\n",
    "  from tsbootstrap import diagnose\n",
    "  diagnose(x).recommended_methods\n",
    "  ```\n",
    "\n",
    "- The which-bootstrap-when tutorial walks through the worked rationale: why\n",
    "  one family beats another on a given series, and how block length and\n",
    "  model order change the answer."
   ]
  }
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