From a85b2eb38fd3e67b8dc4016afe2e98a9c9f318c2 Mon Sep 17 00:00:00 2001
From: FedericoGarza <fede.garza.ramirez@gmail.com>
Date: Thu, 9 Nov 2023 22:39:00 +0000
Subject: [PATCH 1/6] chore: add faq docs

---
 nbs/docs/misc/0_faqs.ipynb | 452 +++++++++++++++++++++++++++++++++++++
 nbs/sidebar.yml            |   2 +
 2 files changed, 454 insertions(+)
 create mode 100644 nbs/docs/misc/0_faqs.ipynb

diff --git a/nbs/docs/misc/0_faqs.ipynb b/nbs/docs/misc/0_faqs.ipynb
new file mode 100644
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--- /dev/null
+++ b/nbs/docs/misc/0_faqs.ipynb
@@ -0,0 +1,452 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9e84913f-7149-47ba-b89d-463527e4e861",
+   "metadata": {},
+   "source": [
+    "# FAQS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8436f5ca-9fac-47c5-982c-3b565ea84de4",
+   "metadata": {},
+   "source": [
+    "## Setting Up Your Authentication Token for Nixtla SDK\n",
+    "\n",
+    "### What is a token?\n",
+    "\n",
+    "A token is a unique string of characters that serves as a key to authenticate your requests when using the Nixtla SDK. It ensures that the person making the requests is allowed to do so.\n",
+    "\n",
+    "### How do I use my token with Nixtla SDK?\n",
+    "\n",
+    "Nixtla will provide you with a personal token upon registration or via your account settings. To integrate this token into your development workflow with the Nixtla SDK, you have two primary methods:\n",
+    "\n",
+    "1. **Direct Copy and Paste:**\n",
+    "   - **Step 1:** Copy the token provided to you by Nixtla.\n",
+    "   - **Step 2:** Instantiate the `TimeGPT` class by directly pasting your token into the code, as shown below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "621dc459-7da5-4cfa-9e11-7d46c6f46fcf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nixtlats import TimeGPT\n",
+    "timegpt = TimeGPT(token='paste your token here')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c6b4125-e547-42cd-8852-8708328005c4",
+   "metadata": {},
+   "source": [
+    "This approach is straightforward and best for quick tests or scripts that won't be shared.\n",
+    "\n",
+    "2. **Using an Environment Variable:**\n",
+    "   - **Step 1:** Store your token in an environment variable named `TIMEGPT_TOKEN`. This can be done for a session or permanently, depending on your preference.\n",
+    "   - **Step 2:** When you instantiate the `TimeGPT` class, it will automatically look for the `TIMEGPT_TOKEN` environment variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4a2ff54-d128-4fb9-bcd0-cf0adef25e33",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#| hide\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed03880a-8a43-4297-9912-19a15535800a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nixtlats import TimeGPT\n",
+    "timegpt = TimeGPT()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba5fc5ee-0e5a-4613-9854-807cb2d7a1eb",
+   "metadata": {},
+   "source": [
+    "   There are several ways to set an environment variable:\n",
+    "\n",
+    "   - **From the Terminal:** Use the export command to set the `TIMEGPT_TOKEN`.\n",
+    "\n",
+    "```bash\n",
+    "export TIMEGPT_TOKEN=your_token\n",
+    "```\n",
+    "\n",
+    "   - **Using a `.env` File:** For a more persistent solution that can be version-controlled if private, or for ease of use across different projects, place your token in a `.env` file.\n",
+    "\n",
+    "```plaintext\n",
+    "# Inside a file named .env\n",
+    "TIMEGPT_TOKEN=your_token\n",
+    "```\n",
+    "\n",
+    "   - **Within Python:** If using a `.env` file, you can load the environment variable within your Python script. Use the `dotenv` package to load the `.env` file, then instantiate the `TimeGPT` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a1966ef-271a-450c-a935-e841a24af498",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv()\n",
+    "from nixtlats import TimeGPT\n",
+    "timegpt = TimeGPT()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8586d2a7-df4f-4d68-87db-e26d44092132",
+   "metadata": {},
+   "source": [
+    "   This approach is more secure and suitable for applications that will be deployed or shared, as it keeps tokens out of the source code.\n",
+    "\n",
+    "Remember, your token is like a password - keep it secret, keep it safe!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abf7effe-3536-4cd2-8469-1feede45a52b",
+   "metadata": {},
+   "source": [
+    "## Long Horizon in Time Series\n",
+    "\n",
+    "When managing long horizon forecasting tasks in time series analysis, understanding the data's frequency and seasonality is crucial. Seasonality refers to periodic fluctuations in time series data that occur at regular intervals, like daily, weekly, or annually. \n",
+    "\n",
+    "### What is Long Horizon? \n",
+    "\n",
+    "- **Definition**: A \"long horizon\" in time series forecasting refers to predictions that extend beyond the range of one or two seasonal cycles. The exact definition depends on the data's frequency and inherent seasonality. For example, with daily data that shows weekly seasonality (7 days), forecasting beyond two weeks would typically be considered a long horizon.\n",
+    "\n",
+    "- **Challenges**: Forecasting over a long horizon is challenging due to the increased uncertainty and the potential influence of many more unknown factors as the forecast period extends. Also, the further out the forecast, the more likely it is that the seasonal patterns may change or be influenced by other factors.\n",
+    "\n",
+    "### How do I use `TimeGPT` for Long Horizon tasks?\n",
+    "\n",
+    "To effectively forecast long horizons, especially when you need to predict more than two seasonal cycles, it's recommended to use specialized models. The `TimeGPT` model in the Nixtla SDK is designed to handle these kinds of tasks:\n",
+    "\n",
+    "- **Model Selection**: Choose the appropriate model variant designed for long horizons. For the Nixtla SDK, this is done by setting the `model` parameter to `'timegpt-1-long-horizon'`.\n",
+    "\n",
+    "- **Forecasting**: Use the `forecast` method to predict the future values of your time series. You can specify the number of periods to forecast (`h`) corresponding to your long-horizon needs.\n",
+    "\n",
+    "Here's how you can implement it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6db4ba4f-cee6-4742-a3c4-2be89780610a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64a13d22-797c-4268-8e15-241f162f9a96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<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>timestamp</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1949-01-01</td>\n",
+       "      <td>112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1949-02-01</td>\n",
+       "      <td>118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1949-03-01</td>\n",
+       "      <td>132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1949-04-01</td>\n",
+       "      <td>129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1949-05-01</td>\n",
+       "      <td>121</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    timestamp  value\n",
+       "0  1949-01-01    112\n",
+       "1  1949-02-01    118\n",
+       "2  1949-03-01    132\n",
+       "3  1949-04-01    129\n",
+       "4  1949-05-01    121"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81094b7d-8241-4006-a424-7adf9ab90dfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:nixtlats.timegpt:Validating inputs...\n",
+      "INFO:nixtlats.timegpt:Preprocessing dataframes...\n",
+      "INFO:nixtlats.timegpt:Inferred freq: MS\n",
+      "WARNING:nixtlats.timegpt:The specified horizon \"h\" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
+      "INFO:nixtlats.timegpt:Calling Forecast Endpoint...\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nixtlats import TimeGPT\n",
+    "timegpt = TimeGPT()\n",
+    "\n",
+    "# df is your time series dataframe\n",
+    "# h is the forecast horizon\n",
+    "# 'timegpt-1-long-horizon' is the model variant for long horizon forecasting\n",
+    "fcst_df = timegpt.forecast(df=df, h=36, model='timegpt-1-long-horizon', time_col='timestamp', target_col='value')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7683ffa-7526-4827-8040-d0e97b4470c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2400x350 with 1 Axes>"
+      ]
+     },
+     "execution_count": null,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "timegpt.plot(df, fcst_df, time_col='timestamp', target_col='value')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f901b49b-c22c-48f0-afc0-240302d33287",
+   "metadata": {},
+   "source": [
+    "**The model argument is also supported by `TimeGPT.cross_validation` and `TimeGPT.detect_anomalies`.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8a9681f-363b-42ae-a8e6-416bf8ee9038",
+   "metadata": {},
+   "source": [
+    "In this example, `df` is your time series data frame, `h=36` would be forecasting for three years ahead, assuming a monthly frequency with a yearly seasonality, which qualifies as a long horizon forecast.\n",
+    "\n",
+    "It's important to note that while the `TimeGPT` model is designed to handle long horizon tasks, the quality of the forecast can still depend on several factors, including data quality, inherent noise in the data, and any external factors that might influence the trend or seasonality over time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae26e5fd-22b6-4abf-afc6-a2d5e4e98cff",
+   "metadata": {},
+   "source": [
+    "## Exogenous variables\n",
+    "\n",
+    "Exogenous variables are external factors that can influence the target variable you are forecasting in a time series model. In the context of the SDK you're using, these exogenous variables are included in the forecasting model to improve the accuracy of the predictions. \n",
+    "\n",
+    "Here's a detailed explanation of how to incorporate exogenous variables in the SDK:\n",
+    "\n",
+    "### How do I use Exogenous Variables in the SDK?\n",
+    "\n",
+    "1. **Prepare Your Data:**\n",
+    "   Ensure that your main dataframe (`df`) contains the historical data including the target variable (`y`) and all exogenous variables that align with the temporal component (`ds`). These exogenous variables (`Exogenous1`, `Exogenous2`, etc.) represent the known values up to the current date.\n",
+    "\n",
+    "2. **Forecasting with Exogenous Variables:**\n",
+    "   To forecast future values, you must also provide the future values of these exogenous variables. This is done with a separate dataframe (`X_df`), which contains the future timestamps and the expected values of the exogenous variables for those times.\n",
+    "\n",
+    "The following image shows graphically the distinction between `df` and `X_df`. `df` must include all the information (historical values of the target variable given by `Historical y` in the plot and the exogenous variables: `Historical Exogenous 1`, and `Historical Exogenous 2`) before the `Forecast Starting Point` given by the vertical black line. Since we want to generate forecasts for `Historical y` (i.e. to fill in the bottom-right part of the plot), we need the future values of the exogenous variables (`Future Exogenous 1`, and `Future Exogenous 2`). That information must be included in `X_df`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79a0eb09-f2b0-4eaa-933b-fc4f54eebd31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| hide\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from pandas.plotting import register_matplotlib_converters\n",
+    "register_matplotlib_converters()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f7087c2-20f3-4181-887c-1a1e3b2a67a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#| echo: false\n",
+    "df = pd.DataFrame({\n",
+    "    'ds': pd.date_range(start='2016-12-01', periods=24, freq='H'),\n",
+    "    'y': 6 * (1_00 * np.array([72.00, 65.80, 59.99, 50.69])).tolist(),\n",
+    "    'Exogenous1': 6 * [61507.0, 59528.0, 58812.0, 57676.0],\n",
+    "    'Exogenous2': 6 * [71066.0, 67311.0, 67470.0, 64529.0]\n",
+    "})\n",
+    "\n",
+    "# Sample future data\n",
+    "X_df = pd.DataFrame({\n",
+    "    'ds': pd.date_range(start='2016-12-02', periods=24, freq='H'),\n",
+    "    'Exogenous1': 6 * [64108.0, 62492.0, 61571.0, 60381.0],\n",
+    "    'Exogenous2': 6 * [70318.0, 67898.0, 68379.0, 64972.0]\n",
+    "})\n",
+    "\n",
+    "# Plot the historical data\n",
+    "plt.figure(figsize=(14, 7))\n",
+    "plt.plot(df['ds'], df['y'], label='Historical y', marker='o')\n",
+    "plt.plot(df['ds'], df['Exogenous1'], label='Historical Exogenous1', marker='x')\n",
+    "plt.plot(df['ds'], df['Exogenous2'], label='Historical Exogenous2', marker='s')\n",
+    "\n",
+    "# Plot the future data\n",
+    "plt.plot(X_df['ds'], X_df['Exogenous1'], label='Future Exogenous1', linestyle='--', marker='x')\n",
+    "plt.plot(X_df['ds'], X_df['Exogenous2'], label='Future Exogenous2', linestyle='--', marker='s')\n",
+    "plt.vlines(x = X_df['ds'].iloc[0], ymin=0, ymax=90_000, colors='black', label='Forecast Starting Point')\n",
+    "\n",
+    "plt.title('Historical and Future Values of the Target and Exogenous Variables')\n",
+    "plt.xlabel('Date')\n",
+    "plt.ylabel('Values')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4bbd048-2def-472e-98ce-54a34049033b",
+   "metadata": {},
+   "source": [
+    "3. **TimeGPT:**\n",
+    "   When calling the `forecast` method, pass the historical dataframe (`df`), specify the horizon (`h`) for the forecast, and pass the future exogenous values `X_df`. The model will automatically consider the exogenous variables in `df` for the historical periods.\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "from nixtlats import TimeGPT\n",
+    "timegpt = TimeGPT()\n",
+    "\n",
+    "# df is your historical dataframe including the target and exogenous variables\n",
+    "# X_df is your future dataframe with expected values for the exogenous variables\n",
+    "# h is the number of periods you want to forecast into the future\n",
+    "forecasted_values = timegpt.forecast(df=df, X_df=X_df, h=21)\n",
+    "```\n",
+    "\n",
+    "#### Note on API Endpoint Usage\n",
+    "\n",
+    "When using direct API endpoints (REST API calls), the approach differs slightly:\n",
+    "\n",
+    "- **Unified Dataframe:** You must concatenate your historical and future exogenous variable data into one unified dataframe (`x`). This dataframe should contain both the past values used for training and the future values for which you want predictions.\n",
+    "  \n",
+    "- **API Payload Structure:** The API expects a payload where the target variable (`y`) and the unified exogenous variables (`x`) are passed separately.\n",
+    "\n",
+    "- **Calling the API:** When making a REST API call, you will typically send a request to the API endpoint with the payload structured as described above.\n",
+    "\n",
+    "Remember that while the SDK abstracts some of the complexities and can automatically handle different dataframes for historical and future values, the API endpoint requires a more manual approach to data preparation."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/nbs/sidebar.yml b/nbs/sidebar.yml
index 3d7bd060..d88dcf15 100644
--- a/nbs/sidebar.yml
+++ b/nbs/sidebar.yml
@@ -11,6 +11,8 @@ website:
         contents: docs/tutorials/*
       - section: "How-To Guides"
         contents: docs/how-to-guides/*
+      - section: "FAQS & Misc"
+        contents: docs/misc/*
       - section: "API Reference"
         contents: 
         - timegpt.ipynb

From 9181d5d5079fa423949d00652d9fe9ac9464b12b Mon Sep 17 00:00:00 2001
From: FedericoGarza <fede.garza.ramirez@gmail.com>
Date: Thu, 9 Nov 2023 22:42:25 +0000
Subject: [PATCH 2/6] chore(readme.io): add misc dir to docs generation

---
 action_files/create_readme_docs.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/action_files/create_readme_docs.sh b/action_files/create_readme_docs.sh
index f3bc1bf1..d2ef4de0 100755
--- a/action_files/create_readme_docs.sh
+++ b/action_files/create_readme_docs.sh
@@ -1,7 +1,7 @@
 #!/bin/bash
 
 BASE_DIR="nbs/docs/"
-SUB_DIRS=("getting-started" "tutorials" "how-to-guides")
+SUB_DIRS=("getting-started" "tutorials" "how-to-guides" "misc")
 
 counter=0
 for sub_dir in "${SUB_DIRS[@]}"; do

From d88cccc00b96cc2d2da0419ae4668e0fb4132ae3 Mon Sep 17 00:00:00 2001
From: FedericoGarza <fede.garza.ramirez@gmail.com>
Date: Thu, 9 Nov 2023 23:01:38 +0000
Subject: [PATCH 3/6] core(fix): add correct svg ignore

---
 action_files/modify_markdown.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/action_files/modify_markdown.py b/action_files/modify_markdown.py
index 305a5b29..2bb43315 100644
--- a/action_files/modify_markdown.py
+++ b/action_files/modify_markdown.py
@@ -88,7 +88,7 @@ def modify_markdown(
 
     # Modify image paths
     content = content.replace("![figure](../../", f"![figure]({host_url}/nbs/")
-    pattern_image = re.compile(r"!\[\]\(((?!\.svg)*?)\)")
+    pattern_image = re.compile(r"!\[\]\(((?!.*\.svg).*?)\)")
     modified_content = pattern_image.sub(
         r"![](" + host_url + dir_path + r"\1)", content
     )

From 3d1c998581d7dec9fb2dee725e1e4bd11d7b677c Mon Sep 17 00:00:00 2001
From: FedericoGarza <fede.garza.ramirez@gmail.com>
Date: Thu, 9 Nov 2023 23:16:29 +0000
Subject: [PATCH 4/6] docs: clean up format

---
 .../1_getting_started_short.ipynb             |   5 +-
 nbs/docs/misc/0_faqs.ipynb                    | 118 +++++++++++++-----
 2 files changed, 94 insertions(+), 29 deletions(-)

diff --git a/nbs/docs/getting-started/1_getting_started_short.ipynb b/nbs/docs/getting-started/1_getting_started_short.ipynb
index c6e406d4..cddd83a5 100644
--- a/nbs/docs/getting-started/1_getting_started_short.ipynb
+++ b/nbs/docs/getting-started/1_getting_started_short.ipynb
@@ -7,7 +7,10 @@
    "source": [
     "# TimeGPT Quickstart\n",
     "\n",
-    "> Unlock the power of accurate predictions and confidently navigate uncertainty. Reduce uncertainty and resource limitations. With TimeGPT, you can effortlessly access state-of-the-art models to make data-driven decisions. Whether you're a bank forecasting market trends or a startup predicting product demand, TimeGPT democratizes access to cutting-edge predictive insights."
+    "> Unlock the power of accurate predictions and confidently navigate uncertainty. Reduce uncertainty and resource limitations.\n",
+    "> With TimeGPT, you can effortlessly access state-of-the-art models to make data-driven decisions. Whether you're a bank\n",
+    "> forecasting market trends or a startup predicting product demand, TimeGPT democratizes access to cutting-edge predictive\n",
+    "> insights."
    ]
   },
   {
diff --git a/nbs/docs/misc/0_faqs.ipynb b/nbs/docs/misc/0_faqs.ipynb
index 6a479043..c96b7486 100644
--- a/nbs/docs/misc/0_faqs.ipynb
+++ b/nbs/docs/misc/0_faqs.ipynb
@@ -33,7 +33,16 @@
    "execution_count": null,
    "id": "621dc459-7da5-4cfa-9e11-7d46c6f46fcf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/miniconda/envs/nixtlats/lib/python3.11/site-packages/statsforecast/core.py:25: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from tqdm.autonotebook import tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "from nixtlats import TimeGPT\n",
     "timegpt = TimeGPT(token='paste your token here')"
@@ -47,8 +56,11 @@
     "This approach is straightforward and best for quick tests or scripts that won't be shared.\n",
     "\n",
     "2. **Using an Environment Variable:**\n",
+    "   \n",
     "   - **Step 1:** Store your token in an environment variable named `TIMEGPT_TOKEN`. This can be done for a session or permanently, depending on your preference.\n",
-    "   - **Step 2:** When you instantiate the `TimeGPT` class, it will automatically look for the `TIMEGPT_TOKEN` environment variable."
+    "\n",
+    "   - **Step 2:**\n",
+    "     When you instantiate the `TimeGPT` class, it will automatically look for the `TIMEGPT_TOKEN` environment variable."
    ]
   },
   {
@@ -88,24 +100,48 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ba5fc5ee-0e5a-4613-9854-807cb2d7a1eb",
+   "id": "6b8b4f8f-a53b-48c3-b3bd-3b7191abbba8",
    "metadata": {},
    "source": [
     "   There are several ways to set an environment variable:\n",
     "\n",
-    "   - **From the Terminal:** Use the export command to set the `TIMEGPT_TOKEN`.\n",
-    "\n",
-    "```bash\n",
+    "   - **From the Terminal:** Use the export command to set the `TIMEGPT_TOKEN`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f0cc628-65a6-4a5d-80cc-7d79d848cdde",
+   "metadata": {},
+   "source": [
+    "``` python\n",
     "export TIMEGPT_TOKEN=your_token\n",
-    "```\n",
-    "\n",
-    "   - **Using a `.env` File:** For a more persistent solution that can be version-controlled if private, or for ease of use across different projects, place your token in a `.env` file.\n",
-    "\n",
-    "```plaintext\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcc7c1e3-d077-4c25-89dc-d863db5bface",
+   "metadata": {},
+   "source": [
+    "   - **Using a `.env` File:** For a more persistent solution that can be version-controlled if private, or for ease of use across different projects, place your token in a `.env` file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19f83214-aef7-484a-9595-1b8999b8fd5c",
+   "metadata": {},
+   "source": [
+    "``` python\n",
     "# Inside a file named .env\n",
     "TIMEGPT_TOKEN=your_token\n",
-    "```\n",
-    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9322665-aabf-427e-be55-95090a7ed293",
+   "metadata": {},
+   "source": [
     "   - **Within Python:** If using a `.env` file, you can load the environment variable within your Python script. Use the `dotenv` package to load the `.env` file, then instantiate the `TimeGPT` class."
    ]
   },
@@ -144,17 +180,27 @@
     "\n",
     "### What is Long Horizon? \n",
     "\n",
-    "- **Definition**: A \"long horizon\" in time series forecasting refers to predictions that extend beyond the range of one or two seasonal cycles. The exact definition depends on the data's frequency and inherent seasonality. For example, with daily data that shows weekly seasonality (7 days), forecasting beyond two weeks would typically be considered a long horizon.\n",
+    "- **Definition**:\n",
+    "  A \"long horizon\" in time series forecasting refers to predictions that extend beyond the range of one or two seasonal\n",
+    "  cycles. The exact definition depends on the data's frequency and inherent seasonality. For example, with daily data that\n",
+    "  shows weekly seasonality (7 days), forecasting beyond two weeks would typically be considered a long horizon.\n",
     "\n",
-    "- **Challenges**: Forecasting over a long horizon is challenging due to the increased uncertainty and the potential influence of many more unknown factors as the forecast period extends. Also, the further out the forecast, the more likely it is that the seasonal patterns may change or be influenced by other factors.\n",
+    "- **Challenges**:\n",
+    "  Forecasting over a long horizon is challenging due to the increased uncertainty and the potential influence of many more\n",
+    "  unknown factors as the forecast period extends. Also, the further out the forecast, the more likely it is that the seasonal\n",
+    "  patterns may change or be influenced by other factors.\n",
     "\n",
     "### How do I use `TimeGPT` for Long Horizon tasks?\n",
     "\n",
     "To effectively forecast long horizons, especially when you need to predict more than two seasonal cycles, it's recommended to use specialized models. The `TimeGPT` model in the Nixtla SDK is designed to handle these kinds of tasks:\n",
     "\n",
-    "- **Model Selection**: Choose the appropriate model variant designed for long horizons. For the Nixtla SDK, this is done by setting the `model` parameter to `'timegpt-1-long-horizon'`.\n",
+    "- **Model Selection**:\n",
+    "  Choose the appropriate model variant designed for long horizons. For the Nixtla SDK, this is done by setting the `model`\n",
+    "  parameter to `'timegpt-1-long-horizon'`.\n",
     "\n",
-    "- **Forecasting**: Use the `forecast` method to predict the future values of your time series. You can specify the number of periods to forecast (`h`) corresponding to your long-horizon needs.\n",
+    "- **Forecasting**:\n",
+    "  Use the `forecast` method to predict the future values of your time series. You can specify the number of periods to\n",
+    "  forecast (`h`) corresponding to your long-horizon needs.\n",
     "\n",
     "Here's how you can implement it:"
    ]
@@ -324,17 +370,22 @@
    "source": [
     "## Exogenous variables\n",
     "\n",
-    "Exogenous variables are external factors that can influence the target variable you are forecasting in a time series model. In the context of the SDK you're using, these exogenous variables are included in the forecasting model to improve the accuracy of the predictions. \n",
+    "Exogenous variables are external factors that can influence the target variable you are forecasting in a time series model. In the context of the SDK you're using, these exogenous variables are included in the forecasting model to improve the accuracy\n",
+    "of the predictions. \n",
     "\n",
     "Here's a detailed explanation of how to incorporate exogenous variables in the SDK:\n",
     "\n",
     "### How do I use Exogenous Variables in the SDK?\n",
     "\n",
     "1. **Prepare Your Data:**\n",
-    "   Ensure that your main dataframe (`df`) contains the historical data including the target variable (`y`) and all exogenous variables that align with the temporal component (`ds`). These exogenous variables (`Exogenous1`, `Exogenous2`, etc.) represent the known values up to the current date.\n",
+    "   Ensure that your main dataframe (`df`) contains the historical data including the target variable (`y`) and all exogenous\n",
+    "   variables that align with the temporal component (`ds`). These exogenous variables (`Exogenous1`, `Exogenous2`, etc.)\n",
+    "   represent the known values up to the current date.\n",
     "\n",
-    "2. **Forecasting with Exogenous Variables:**\n",
-    "   To forecast future values, you must also provide the future values of these exogenous variables. This is done with a separate dataframe (`X_df`), which contains the future timestamps and the expected values of the exogenous variables for those times.\n",
+    "3. **Forecasting with Exogenous Variables:**\n",
+    "   To forecast future values, you must also provide the future values of these exogenous variables. This is done with a\n",
+    "   separate dataframe (`X_df`), which contains the future timestamps and the expected values of the exogenous variables for\n",
+    "   those times.\n",
     "\n",
     "The following image shows graphically the distinction between `df` and `X_df`. `df` must include all the information (historical values of the target variable given by `Historical y` in the plot and the exogenous variables: `Historical Exogenous 1`, and `Historical Exogenous 2`) before the `Forecast Starting Point` given by the vertical black line. Since we want to generate forecasts for `Historical y` (i.e. to fill in the bottom-right part of the plot), we need the future values of the exogenous variables (`Future Exogenous 1`, and `Future Exogenous 2`). That information must be included in `X_df`. "
    ]
@@ -409,14 +460,19 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c4bbd048-2def-472e-98ce-54a34049033b",
+   "id": "a208f0bf-886e-49c5-a552-d1f634f6d068",
    "metadata": {},
    "source": [
     "3. **TimeGPT:**\n",
-    "   When calling the `forecast` method, pass the historical dataframe (`df`), specify the horizon (`h`) for the forecast, and pass the future exogenous values `X_df`. The model will automatically consider the exogenous variables in `df` for the historical periods.\n",
-    "\n",
-    "\n",
-    "```python\n",
+    "   When calling the `forecast` method, pass the historical dataframe (`df`), specify the horizon (`h`) for the forecast, and pass the future exogenous values `X_df`. The model will automatically consider the exogenous variables in `df` for the historical periods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b221f0b-a82d-4c7b-bc9a-dec801e5424d",
+   "metadata": {},
+   "source": [
+    "``` python\n",
     "from nixtlats import TimeGPT\n",
     "timegpt = TimeGPT()\n",
     "\n",
@@ -424,8 +480,14 @@
     "# X_df is your future dataframe with expected values for the exogenous variables\n",
     "# h is the number of periods you want to forecast into the future\n",
     "forecasted_values = timegpt.forecast(df=df, X_df=X_df, h=21)\n",
-    "```\n",
-    "\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86f3c28e-adab-4551-a235-d69280061f6e",
+   "metadata": {},
+   "source": [
     "#### Note on API Endpoint Usage\n",
     "\n",
     "When using direct API endpoints (REST API calls), the approach differs slightly:\n",

From 1d487cc7319c3c5e9b185f3621d2809cb6075afd Mon Sep 17 00:00:00 2001
From: FedericoGarza <fede.garza.ramirez@gmail.com>
Date: Thu, 9 Nov 2023 23:26:12 +0000
Subject: [PATCH 5/6] core(readme.io): parse bash code block correctly

---
 action_files/modify_markdown.py |  2 +-
 nbs/docs/misc/0_faqs.ipynb      | 16 +++++++++++-----
 2 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/action_files/modify_markdown.py b/action_files/modify_markdown.py
index 2bb43315..7b2ffa0b 100644
--- a/action_files/modify_markdown.py
+++ b/action_files/modify_markdown.py
@@ -16,7 +16,7 @@ def to_snake_case(s):
 
 
 def merge_lines(md_text):
-    code_block_pattern = re.compile(r"``` python([\s\S]*?)```", re.MULTILINE)
+    code_block_pattern = re.compile(r"``` (?:python|bash)([\s\S]*?)```", re.MULTILINE)
     code_blocks = code_block_pattern.findall(md_text)
     md_text_no_code = code_block_pattern.sub("CODEBLOCK", md_text)
     lines = md_text_no_code.split("\n")
diff --git a/nbs/docs/misc/0_faqs.ipynb b/nbs/docs/misc/0_faqs.ipynb
index c96b7486..5a15097b 100644
--- a/nbs/docs/misc/0_faqs.ipynb
+++ b/nbs/docs/misc/0_faqs.ipynb
@@ -113,7 +113,7 @@
    "id": "6f0cc628-65a6-4a5d-80cc-7d79d848cdde",
    "metadata": {},
    "source": [
-    "``` python\n",
+    "``` bash\n",
     "export TIMEGPT_TOKEN=your_token\n",
     "```"
    ]
@@ -131,7 +131,7 @@
    "id": "19f83214-aef7-484a-9595-1b8999b8fd5c",
    "metadata": {},
    "source": [
-    "``` python\n",
+    "``` bash\n",
     "# Inside a file named .env\n",
     "TIMEGPT_TOKEN=your_token\n",
     "```"
@@ -365,7 +365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ae26e5fd-22b6-4abf-afc6-a2d5e4e98cff",
+   "id": "5b1ed3e4-1eef-4cfd-ab81-4e8c4b57d082",
    "metadata": {},
    "source": [
     "## Exogenous variables\n",
@@ -385,8 +385,14 @@
     "3. **Forecasting with Exogenous Variables:**\n",
     "   To forecast future values, you must also provide the future values of these exogenous variables. This is done with a\n",
     "   separate dataframe (`X_df`), which contains the future timestamps and the expected values of the exogenous variables for\n",
-    "   those times.\n",
-    "\n",
+    "   those times."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ea505a1-0f87-443e-b820-6462267e35b7",
+   "metadata": {},
+   "source": [
     "The following image shows graphically the distinction between `df` and `X_df`. `df` must include all the information (historical values of the target variable given by `Historical y` in the plot and the exogenous variables: `Historical Exogenous 1`, and `Historical Exogenous 2`) before the `Forecast Starting Point` given by the vertical black line. Since we want to generate forecasts for `Historical y` (i.e. to fill in the bottom-right part of the plot), we need the future values of the exogenous variables (`Future Exogenous 1`, and `Future Exogenous 2`). That information must be included in `X_df`. "
    ]
   },

From 00edd773ad5377a3bf97d116202f3f2e4c39e578 Mon Sep 17 00:00:00 2001
From: FedericoGarza <fede.garza.ramirez@gmail.com>
Date: Thu, 9 Nov 2023 23:30:29 +0000
Subject: [PATCH 6/6] fix: rm exceprt

---
 action_files/modify_markdown.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/action_files/modify_markdown.py b/action_files/modify_markdown.py
index 7b2ffa0b..1fb637aa 100644
--- a/action_files/modify_markdown.py
+++ b/action_files/modify_markdown.py
@@ -75,7 +75,6 @@ def modify_markdown(
 title: "{title}"
 slug: "{slug}"
 order: {slug_number}
-excerpt: "Learn how to do {title} with TimeGPT"
 category: {category}
 hidden: false
 ---