documentation update

R/R/prophet.R

@@ -444,7 +444,8 @@ make_holiday_features <- function(m, dates) {
 #' components.
 #'
 #' Increasing the number of Fourier components allows the seasonality to change
-#' more quickly (at risk of overfitting).
+#' more quickly (at risk of overfitting). Default values for yearly and weekly
+#' seasonalities are 10 and 3 respectively.
 #'
 #' @param m Prophet object.
 #' @param name String name of the seasonality component.

notebooks/quick_start.ipynb

@@ -49,9 +49,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -125,9 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "m = Prophet()\n",
@@ -144,9 +140,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -213,9 +207,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -300,9 +292,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -329,9 +319,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -352,6 +340,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "More details about the options available for each method are available in the docstrings, for example, via `help(Prophet)` or `help(Prophet.fit)`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## R API\n",
     "\n",
     "In R, we use the normal model fitting API.  We provide a `prophet` function that performs fitting and returns a model object.  You can then call `predict` and `plot` on this model object."
@@ -361,7 +356,6 @@
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "collapsed": false,
     "output_hidden": true
    },
    "outputs": [
@@ -408,9 +402,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%%R\n",
@@ -429,7 +421,6 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
-    "collapsed": false,
     "output_hidden": true
    },
    "outputs": [
@@ -471,9 +462,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -507,9 +496,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -543,9 +530,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -570,9 +555,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -586,6 +569,13 @@
     "%%R -w 9 -h 9 -u in\n",
     "prophet_plot_components(m, forecast)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More details about the options available for each method are available in the docstrings, for example, via `?prophet` or `?fit.prophet`. This documentation is also available in the [reference manual](https://cran.r-project.org/web/packages/prophet/prophet.pdf) on CRAN."
+   ]
   }
  ],
  "metadata": {
@@ -608,5 +598,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

python/fbprophet/forecaster.py

@@ -443,7 +443,8 @@ class Prophet(object):
         components.
 
         Increasing the number of Fourier components allows the seasonality to
-        change more quickly (at risk of overfitting).
+        change more quickly (at risk of overfitting). Default values for yearly
+        and weekly seasonalities are 10 and 3 respectively.
 
         Parameters
         ----------