style/rendering updates

Author: FilippoMB

notebooks/00/intro.md

@@ -19,11 +19,6 @@ To reinforce learning and encourage active engagement, each chapter concludes wi
 
 Whether you are new to time series analysis or looking to refine your expertise, this course offers a broad exploration of the field, with Python as your toolkit. I hope that you will find this material both educational and entertaining, brining you a step closer to mastering time series analysis.
 
-```{note}
-The notebooks are presented in class as slides using RISE (see [here](https://github.com/FilippoMB/python-time-series-handbook?tab=readme-ov-file#-notebook-format-and-slides) for more details). 
-For this reason, the text in the notebooks is organized with bullet points.
-```
-
 
 ## 📖 Chapters
 
@@ -32,6 +27,11 @@ The course is organized into the following chapters.
 ```{tableofcontents}
 ```
 
+```{note}
+The notebooks are presented in class as slides using RISE (see [here](https://github.com/FilippoMB/python-time-series-handbook?tab=readme-ov-file#-notebook-format-and-slides) for more details). 
+For this reason, the text in the notebooks is organized with bullet points.
+```
+
 ## 🎓 University courses
 
 These notebooks are currently adopted in [STA-2003 Tidsrekker](https://sa.uit.no/utdanning/emner/emne?p_document_id=822793) at UiT the Arctic University of Tromsø.

notebooks/01/introduction_to_time_series.ipynb

@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 1,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -389,7 +389,10 @@
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [
     {
@@ -470,7 +473,10 @@
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
-    }
+    },
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [
     {
@@ -527,7 +533,10 @@
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [
     {
@@ -623,7 +632,10 @@
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [
     {
@@ -686,7 +698,10 @@
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [
     {
@@ -761,7 +776,10 @@
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
-    }
+    },
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [
     {
@@ -837,6 +855,21 @@
      "slide_type": "fragment"
     }
    },
+   "outputs": [],
+   "source": [
+    "slope, intercept = np.polyfit(np.arange(len(additive)), additive, 1) # estimate line coefficient\n",
+    "trend = np.arange(len(additive)) * slope + intercept # linear trend\n",
+    "detrended = additive - trend # remove the trend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "tags": [
+     "hide-input"
+    ]
+   },
    "outputs": [
     {
      "data": {
@@ -850,10 +883,6 @@
     }
    ],
    "source": [
-    "slope, intercept = np.polyfit(np.arange(len(additive)), additive, 1) # estimate line coefficient\n",
-    "trend = np.arange(len(additive)) * slope + intercept # linear trend\n",
-    "detrended = additive - trend # remove the trend\n",
-    "\n",
     "plt.figure(figsize=(10, 3))\n",
     "plt.plot(additive, label='Original')\n",
     "plt.plot(trend, label='Trend')\n",
@@ -892,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -904,27 +933,20 @@
     "additive_decomposition = seasonal_decompose(x=additive, model='additive', period=12)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "- We define a utility function to make the plots."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
-    }
+    },
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [],
    "source": [
+    "# Utility function to make the plots\n",
     "def seas_decomp_plots(original, decomposition):\n",
     "    _, axes = plt.subplots(4, 1, sharex=True, sharey=False, figsize=(7, 5))\n",
     "    axes[0].plot(original, label='Original')\n",
@@ -940,7 +962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1008,7 +1030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1054,13 +1076,13 @@
    },
    "source": [
     "### Locally estimated scatterplot smoothing (LOESS)\n",
-    "- Next, we try a second method called `STL` (Seasonal and Trend decomposition using LOESS)\n",
+    "- Next, we try a second method called `STL` (Seasonal and Trend decomposition using LOESS).\n",
     "- We start with the additive model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1097,7 +1119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1219,17 +1241,19 @@
     "    - Use the Fast Fourier Transform on a signal *without* trend.\n",
     "\n",
     "- We will look more into FFT later on.\n",
-    "- For now, you can use the following code to compute the dominant period in the data."
+    "- For now, you can use the following function to compute the dominant period in the data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
     },
-    "tags": []
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [],
    "source": [
@@ -1260,7 +1284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -1282,12 +1306,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
     },
-    "tags": []
+    "tags": [
+     "hide-input"
+    ]
    },
    "outputs": [
     {
@@ -1325,7 +1351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1338,7 +1364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1435,7 +1461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "-"

notebooks/02/stationarity.ipynb

@@ -234,13 +234,14 @@
    },
    "outputs": [],
    "source": [
-    "def run_sequence_plot(x, y, title, xlabel=\"Time\", ylabel=\"Values\"):\n",
-    "    fig, ax = plt.subplots(1,1, figsize=(10,3.5))\n",
+    "def run_sequence_plot(x, y, title, xlabel=\"Time\", ylabel=\"Values\", ax=None):\n",
+    "    if ax is None:\n",
+    "        _, ax = plt.subplots(1,1, figsize=(10, 3.5))\n",
     "    ax.plot(x, y, 'k-')\n",
     "    ax.set_title(title)\n",
     "    ax.set_xlabel(xlabel)\n",
     "    ax.set_ylabel(ylabel)\n",
-    "    plt.grid(alpha=0.3)\n",
+    "    ax.grid(alpha=0.3)\n",
     "    return ax"
    ]
   },