{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "92f1abae",
   "metadata": {},
   "source": [
    "# BaseAttentive: Hybrid vs Transformer Architectures\n",
    "\n",
    "This notebook compares the two main architectural choices in BaseAttentive:\n",
    "- **Hybrid**: Multi-scale LSTM + Attention (captures temporal patterns + global context)\n",
    "- **Transformer**: Pure self-attention (global context only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "52c08fe3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-21T01:44:40.341143Z",
     "iopub.status.busy": "2026-04-21T01:44:40.341143Z",
     "iopub.status.idle": "2026-04-21T01:44:44.173934Z",
     "shell.execute_reply": "2026-04-21T01:44:44.172875Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backend: tensorflow\n"
     ]
    }
   ],
   "source": [
    "# ── v2.2.0 Backend Setup ─────────────────────────────────────────────────────\n",
    "# BASE_ATTENTIVE_BACKEND must be set *before* importing base_attentive.\n",
    "# Choose your installed backend: \"tensorflow\" | \"torch\" | \"jax\" | \"auto\"\n",
    "import os\n",
    "os.environ.setdefault(\"BASE_ATTENTIVE_BACKEND\", \"tensorflow\")\n",
    "os.environ.setdefault(\"KERAS_BACKEND\", os.environ[\"BASE_ATTENTIVE_BACKEND\"])\n",
    "import keras  # initialise Keras 3 backend before base_attentive\n",
    "BACKEND = os.environ[\"BASE_ATTENTIVE_BACKEND\"]\n",
    "print(f\"Backend: {BACKEND}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b29bc4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-21T01:44:44.176055Z",
     "iopub.status.busy": "2026-04-21T01:44:44.176055Z",
     "iopub.status.idle": "2026-04-21T01:44:44.220445Z",
     "shell.execute_reply": "2026-04-21T01:44:44.219245Z"
    }
   },
   "outputs": [],
   "source": [
    "from base_attentive import BaseAttentive"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c67e81e7",
   "metadata": {},
   "source": [
    "## Common Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ec0cc42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-21T01:44:44.222572Z",
     "iopub.status.busy": "2026-04-21T01:44:44.222572Z",
     "iopub.status.idle": "2026-04-21T01:44:44.236158Z",
     "shell.execute_reply": "2026-04-21T01:44:44.234978Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Shared parameters defined\n"
     ]
    }
   ],
   "source": [
    "# Shared parameters\n",
    "STATIC_DIM = 4\n",
    "DYNAMIC_DIM = 8\n",
    "FUTURE_DIM = 6\n",
    "OUTPUT_DIM = 1\n",
    "FORECAST_HORIZON = 24\n",
    "\n",
    "shared_params = {\n",
    "    \"static_input_dim\": STATIC_DIM,\n",
    "    \"dynamic_input_dim\": DYNAMIC_DIM,\n",
    "    \"future_input_dim\": FUTURE_DIM,\n",
    "    \"output_dim\": OUTPUT_DIM,\n",
    "    \"forecast_horizon\": FORECAST_HORIZON,\n",
    "    \"embed_dim\": 32,\n",
    "    \"attention_units\": 64,\n",
    "    \"num_heads\": 8,\n",
    "    \"dropout_rate\": 0.1,\n",
    "}\n",
    "\n",
    "print(\"✅ Shared parameters defined\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff720c42",
   "metadata": {},
   "source": [
    "## Model 1: Hybrid Architecture (LSTM + Attention)\n",
    "\n",
    "**Characteristics:**\n",
    "- Multi-scale LSTM for hierarchical temporal feature extraction\n",
    "- Attention mechanisms for global context\n",
    "- **Best for**: Complex temporal patterns, shorter sequences\n",
    "- **Pros**: Captures temporal correlations well\n",
    "- **Cons**: Slower training/inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a9b1251f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-21T01:44:44.238468Z",
     "iopub.status.busy": "2026-04-21T01:44:44.238468Z",
     "iopub.status.idle": "2026-04-21T01:44:44.550196Z",
     "shell.execute_reply": "2026-04-21T01:44:44.548193Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\projects\\base-attentive\\src\\base_attentive\\core\\base_attentive.py:148: DeprecatedParameterWarning: BaseAttentive: 'static_input_dim' is deprecated since 2.1.0 and will be removed in 3.0.0. Use 'static_dim' instead.\n",
      "  resolved = resolve_deprecated_kwargs(\n",
      "D:\\projects\\base-attentive\\src\\base_attentive\\core\\base_attentive.py:148: DeprecatedParameterWarning: BaseAttentive: 'dynamic_input_dim' is deprecated since 2.1.0 and will be removed in 3.0.0. Use 'dynamic_dim' instead.\n",
      "  resolved = resolve_deprecated_kwargs(\n",
      "D:\\projects\\base-attentive\\src\\base_attentive\\core\\base_attentive.py:148: DeprecatedParameterWarning: BaseAttentive: 'future_input_dim' is deprecated since 2.1.0 and will be removed in 3.0.0. Use 'future_dim' instead.\n",
      "  resolved = resolve_deprecated_kwargs(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔧 Hybrid Model Configuration:\n",
      "  Objective: hybrid\n",
      "  LSTM Units: 64\n",
      "  Scales: None\n",
      "  Mode: None\n"
     ]
    }
   ],
   "source": [
    "# Hybrid model: LSTM + Attention\n",
    "hybrid_config = {\n",
    "    **shared_params,\n",
    "    \"objective\": \"hybrid\",  # Uses LSTM encoder\n",
    "    \"name\": \"HybridModel\",\n",
    "}\n",
    "\n",
    "hybrid_model = BaseAttentive(**hybrid_config)\n",
    "\n",
    "print(\"🔧 Hybrid Model Configuration:\")\n",
    "print(f\"  Objective: {hybrid_model.objective}\")\n",
    "print(f\"  LSTM Units: {hybrid_model.lstm_units}\")\n",
    "print(f\"  Scales: {hybrid_model.scales}\")\n",
    "print(f\"  Mode: {hybrid_model.mode}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69ee3349",
   "metadata": {},
   "source": [
    "## Model 2: Transformer Architecture (Pure Attention)\n",
    "\n",
    "**Characteristics:**\n",
    "- Pure self-attention stack, no recurrence\n",
    "- Parallel computation across sequence\n",
    "- **Best for**: Long sequences, global dependencies\n",
    "- **Pros**: Faster, captures long-range dependencies\n",
    "- **Cons**: May miss local temporal patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20b858f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-21T01:44:44.552209Z",
     "iopub.status.busy": "2026-04-21T01:44:44.552209Z",
     "iopub.status.idle": "2026-04-21T01:44:44.737353Z",
     "shell.execute_reply": "2026-04-21T01:44:44.736751Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔧 Transformer Model Configuration:\n",
      "  Objective: transformer\n",
      "  Encoder Layers: 2\n",
      "  Attention Units: 64\n",
      "  Mode: None\n"
     ]
    }
   ],
   "source": [
    "# Transformer model: Pure Attention\n",
    "transformer_config = {\n",
    "    **shared_params,\n",
    "    \"objective\": \"transformer\",  # Pure attention encoder\n",
    "    \"num_encoder_layers\": 2,  # Number of attention blocks\n",
    "    \"name\": \"TransformerModel\",\n",
    "}\n",
    "\n",
    "transformer_model = BaseAttentive(**transformer_config)\n",
    "\n",
    "print(\"🔧 Transformer Model Configuration:\")\n",
    "print(f\"  Objective: {transformer_model.objective}\")\n",
    "print(\n",
    "    f\"  Encoder Layers: {transformer_model.num_encoder_layers}\"\n",
    ")\n",
    "print(\n",
    "    f\"  Attention Units: {transformer_model.attention_units}\"\n",
    ")\n",
    "print(f\"  Mode: {transformer_model.mode}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66749759",
   "metadata": {},
   "source": [
    "## Comparison Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3056d5f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-21T01:44:44.739711Z",
     "iopub.status.busy": "2026-04-21T01:44:44.739711Z",
     "iopub.status.idle": "2026-04-21T01:44:44.753655Z",
     "shell.execute_reply": "2026-04-21T01:44:44.752303Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Aspect    Hybrid (LSTM+Attn) Transformer (Pure Attn)\n",
      "           Encoder Type      Multi-scale LSTM    Self-Attention Stack\n",
      "       Speed (Training)                Slower                  Faster\n",
      "      Speed (Inference)                Slower                  Faster\n",
      "      Temporal Patterns             Excellent                    Good\n",
      "Long-Range Dependencies                  Good               Excellent\n",
      "           Memory Usage                Higher                   Lower\n",
      "               Best For Complex temporal data          Long sequences\n",
      "        Sequence Length       Short to medium                    Long\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "comparison_data = {\n",
    "    \"Aspect\": [\n",
    "        \"Encoder Type\",\n",
    "        \"Speed (Training)\",\n",
    "        \"Speed (Inference)\",\n",
    "        \"Temporal Patterns\",\n",
    "        \"Long-Range Dependencies\",\n",
    "        \"Memory Usage\",\n",
    "        \"Best For\",\n",
    "        \"Sequence Length\",\n",
    "    ],\n",
    "    \"Hybrid (LSTM+Attn)\": [\n",
    "        \"Multi-scale LSTM\",\n",
    "        \"Slower\",\n",
    "        \"Slower\",\n",
    "        \"Excellent\",\n",
    "        \"Good\",\n",
    "        \"Higher\",\n",
    "        \"Complex temporal data\",\n",
    "        \"Short to medium\",\n",
    "    ],\n",
    "    \"Transformer (Pure Attn)\": [\n",
    "        \"Self-Attention Stack\",\n",
    "        \"Faster\",\n",
    "        \"Faster\",\n",
    "        \"Good\",\n",
    "        \"Excellent\",\n",
    "        \"Lower\",\n",
    "        \"Long sequences\",\n",
    "        \"Long\",\n",
    "    ],\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(comparison_data)\n",
    "print(df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8676200",
   "metadata": {},
   "source": [
    "## Advanced Configuration: Using architecture_config\n",
    "\n",
    "You can also use `architecture_config` for fine-grained control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7b45d1e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-21T01:44:44.756089Z",
     "iopub.status.busy": "2026-04-21T01:44:44.756089Z",
     "iopub.status.idle": "2026-04-21T01:44:44.830964Z",
     "shell.execute_reply": "2026-04-21T01:44:44.829808Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🎨 Custom Architecture Configuration:\n",
      "  Encoder Type: transformer\n",
      "  Attention Stack: ['cross', 'hierarchical']\n",
      "  Feature Processing: dense\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\projects\\base-attentive\\src\\base_attentive\\core\\base_attentive.py:148: DeprecatedParameterWarning: BaseAttentive: 'static_input_dim' is deprecated since 2.1.0 and will be removed in 3.0.0. Use 'static_dim' instead.\n",
      "  resolved = resolve_deprecated_kwargs(\n",
      "D:\\projects\\base-attentive\\src\\base_attentive\\core\\base_attentive.py:148: DeprecatedParameterWarning: BaseAttentive: 'dynamic_input_dim' is deprecated since 2.1.0 and will be removed in 3.0.0. Use 'dynamic_dim' instead.\n",
      "  resolved = resolve_deprecated_kwargs(\n",
      "D:\\projects\\base-attentive\\src\\base_attentive\\core\\base_attentive.py:148: DeprecatedParameterWarning: BaseAttentive: 'future_input_dim' is deprecated since 2.1.0 and will be removed in 3.0.0. Use 'future_dim' instead.\n",
      "  resolved = resolve_deprecated_kwargs(\n"
     ]
    }
   ],
   "source": [
    "# Advanced configuration with architecture_config\n",
    "custom_architecture = {\n",
    "    \"encoder_type\": \"transformer\",\n",
    "    \"decoder_attention_stack\": [\n",
    "        \"cross\",\n",
    "        \"hierarchical\",\n",
    "    ],  # Skip memory attention\n",
    "    \"feature_processing\": \"dense\",  # Use dense instead of VSN\n",
    "}\n",
    "\n",
    "custom_model = BaseAttentive(\n",
    "    **shared_params,\n",
    "    architecture_config=custom_architecture,\n",
    "    name=\"CustomModel\",\n",
    ")\n",
    "\n",
    "print(\"🎨 Custom Architecture Configuration:\")\n",
    "print(\n",
    "    f\"  Encoder Type: {custom_model.architecture_config.get('encoder_type')}\"\n",
    ")\n",
    "print(\n",
    "    f\"  Attention Stack: {custom_model.architecture_config.get('decoder_attention_stack')}\"\n",
    ")\n",
    "print(\n",
    "    f\"  Feature Processing: {custom_model.architecture_config.get('feature_processing')}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beca32ef",
   "metadata": {},
   "source": [
    "## Training Both Architectures\n",
    "\n",
    "Train Hybrid and Transformer models on identical synthetic data to compare convergence and forecast quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "286f24e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-21T01:44:44.835529Z",
     "iopub.status.busy": "2026-04-21T01:44:44.834394Z",
     "iopub.status.idle": "2026-04-21T01:44:44.862513Z",
     "shell.execute_reply": "2026-04-21T01:44:44.861337Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data — static:(64, 4)  dynamic:(64, 20, 8)  future:(64, 24, 6)  target:(64, 24, 1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import keras\n",
    "\n",
    "np.random.seed(42)\n",
    "N, T, H = 64, 20, 24  # samples, lookback, horizon\n",
    "\n",
    "# ── Synthetic sine-wave data ──────────────────────────────────────────\n",
    "t_past   = np.linspace(0, 4*np.pi, T)\n",
    "t_future = np.linspace(4*np.pi, 6*np.pi, H)\n",
    "noise    = lambda s: np.random.randn(*s).astype('float32') * 0.15\n",
    "\n",
    "static_d  = np.random.randn(N, STATIC_DIM ).astype('float32')\n",
    "dynamic_d = (np.tile(np.sin(t_past),   (N, 1))[:, :, None]\n",
    "             * np.random.rand(N, 1, DYNAMIC_DIM) + noise((N, T, DYNAMIC_DIM)))\n",
    "future_d  = (np.tile(np.cos(t_future), (N, 1))[:, :, None]\n",
    "             * np.random.rand(N, 1, FUTURE_DIM)  + noise((N, H, FUTURE_DIM)))\n",
    "target_d  = (np.tile(np.sin(t_future), (N, 1))[:, :, None]\n",
    "             + noise((N, H, OUTPUT_DIM))).astype('float32')\n",
    "\n",
    "print(f'Data — static:{static_d.shape}  dynamic:{dynamic_d.shape}  future:{future_d.shape}  target:{target_d.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2af42b3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-21T01:44:44.865690Z",
     "iopub.status.busy": "2026-04-21T01:44:44.864690Z",
     "iopub.status.idle": "2026-04-21T01:46:01.931638Z",
     "shell.execute_reply": "2026-04-21T01:46:01.931638Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "Hybrid (LSTM + Attention)       train MSE=0.0763  val MSE=0.0417\n",
      "Transformer (Pure Attention)    train MSE=0.0725  val MSE=0.0344\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "def compile_and_train(model, label, epochs=12):\n",
    "    _ = model([static_d, dynamic_d, future_d])  # build weights\n",
    "    model.compile(optimizer=keras.optimizers.Adam(1e-3), loss='mse', metrics=['mae'])\n",
    "    h = model.fit(\n",
    "        [static_d, dynamic_d, future_d], target_d,\n",
    "        epochs=epochs, batch_size=16, validation_split=0.2, verbose=0,\n",
    "    )\n",
    "    print(f'{label:30s}  train MSE={h.history[\"loss\"][-1]:.4f}  val MSE={h.history[\"val_loss\"][-1]:.4f}')\n",
    "    return h\n",
    "\n",
    "print('Training...')\n",
    "h_hybrid      = compile_and_train(hybrid_model,      'Hybrid (LSTM + Attention)')\n",
    "h_transformer = compile_and_train(transformer_model, 'Transformer (Pure Attention)')\n",
    "print('Done.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e02c30b",
   "metadata": {},
   "source": [
    "## Plot 1 — Training Loss Curves\n",
    "\n",
    "Side-by-side convergence curves show which architecture reaches lower loss faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "405d36ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-21T01:46:01.934017Z",
     "iopub.status.busy": "2026-04-21T01:46:01.934017Z",
     "iopub.status.idle": "2026-04-21T01:46:02.199476Z",
     "shell.execute_reply": "2026-04-21T01:46:02.196392Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "for ax, h, label, color in zip(\n",
    "    axes,\n",
    "    [h_hybrid, h_transformer],\n",
    "    ['Hybrid (LSTM+Attn)', 'Transformer'],\n",
    "    ['steelblue', 'darkorange'],\n",
    "):\n",
    "    ax.plot(h.history['loss'],     label='Train', color=color,  linewidth=2)\n",
    "    ax.plot(h.history['val_loss'], label='Val',   color=color,  linewidth=2, linestyle='--', alpha=0.7)\n",
    "    ax.set_title(f'{label} — Loss', fontsize=12)\n",
    "    ax.set_xlabel('Epoch')\n",
    "    ax.set_ylabel('MSE')\n",
    "    ax.legend()\n",
    "    ax.grid(True, alpha=0.3)\n",
    "\n",
    "plt.suptitle('Training Convergence: Hybrid vs Transformer', fontsize=13)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "143d26de",
   "metadata": {},
   "source": [
    "## Plot 2 — Forecast vs Actual\n",
    "\n",
    "Overlay both models' predictions against the true target for the same test sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aca7874d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-04-21T01:46:02.202064Z",
     "iopub.status.busy": "2026-04-21T01:46:02.202064Z",
     "iopub.status.idle": "2026-04-21T01:46:04.858545Z",
     "shell.execute_reply": "2026-04-21T01:46:04.857387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hybrid      MAE: 0.1593\n",
      "Transformer MAE: 0.1481\n"
     ]
    }
   ],
   "source": [
    "# Predict with both models (use last 10 samples as 'test')\n",
    "test_slice = slice(-10, None)\n",
    "pred_h = hybrid_model.predict(\n",
    "    [static_d[test_slice], dynamic_d[test_slice], future_d[test_slice]], verbose=0)\n",
    "pred_t = transformer_model.predict(\n",
    "    [static_d[test_slice], dynamic_d[test_slice], future_d[test_slice]], verbose=0)\n",
    "\n",
    "sample = 0\n",
    "steps  = np.arange(1, H + 1)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 4))\n",
    "ax.plot(steps, target_d[test_slice][sample, :, 0],\n",
    "        label='Actual',      color='black',      linewidth=2.5)\n",
    "ax.plot(steps, pred_h[sample, :, 0],\n",
    "        label='Hybrid',      color='steelblue',  linewidth=2, linestyle='--')\n",
    "ax.plot(steps, pred_t[sample, :, 0],\n",
    "        label='Transformer', color='darkorange', linewidth=2, linestyle=':')\n",
    "\n",
    "ax.set_title('Forecast vs Actual — Hybrid vs Transformer', fontsize=13)\n",
    "ax.set_xlabel('Forecast step')\n",
    "ax.set_ylabel('Value')\n",
    "ax.legend()\n",
    "ax.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f'Hybrid      MAE: {float(np.mean(np.abs(pred_h - target_d[test_slice]))):.4f}')\n",
    "print(f'Transformer MAE: {float(np.mean(np.abs(pred_t - target_d[test_slice]))):.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8384b61",
   "metadata": {},
   "source": [
    "## Recommendation\n",
    "\n",
    "**Use Hybrid when:**\n",
    "- Your data has strong short-term temporal correlations\n",
    "- Sequences are short to medium length (< 50 steps)\n",
    "- You need to capture multi-scale temporal patterns\n",
    "\n",
    "**Use Transformer when:**\n",
    "- Long-range dependencies are important\n",
    "- Sequences are long (> 100 steps)\n",
    "- You prioritize speed and have sufficient compute\n",
    "- Your data is mostly stationary with trend/seasonality"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}