README.md in prophet-rb-0.1.1 vs README.md in prophet-rb-0.2.0
- old
+ new
@@ -8,11 +8,11 @@
- Linear and non-linear growth
- Holidays and special events
And gracefully handles missing data
-[](https://travis-ci.org/ankane/prophet)
+[](https://travis-ci.org/ankane/prophet) [](https://ci.appveyor.com/project/ankane/prophet/branch/master)
## Installation
Add this line to your application’s Gemfile:
@@ -29,11 +29,11 @@
[Explanation](https://facebook.github.io/prophet/docs/quick_start.html)
Create a data frame with `ds` and `y` columns - here’s [an example](examples/example_wp_log_peyton_manning.csv) you can use
```ruby
-df = Daru::DataFrame.from_csv("example_wp_log_peyton_manning.csv")
+df = Rover.read_csv("example_wp_log_peyton_manning.csv")
df.head(5)
```
ds | y
--- | ---
@@ -105,11 +105,11 @@
[Explanation](https://facebook.github.io/prophet/docs/saturating_forecasts.html)
Forecast logistic growth instead of linear
```ruby
-df = Daru::DataFrame.from_csv("example_wp_log_R.csv")
+df = Rover.read_csv("example_wp_log_R.csv")
df["cap"] = 8.5
m = Prophet.new(growth: "logistic")
m.fit(df)
future = m.make_future_dataframe(periods: 365)
future["cap"] = 8.5
@@ -144,21 +144,21 @@
[Explanation](https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html)
Create a data frame with `holiday` and `ds` columns. Include all occurrences in your past data and future occurrences you’d like to forecast.
```ruby
-playoffs = Daru::DataFrame.new(
+playoffs = Rover::DataFrame.new(
"holiday" => ["playoff"] * 14,
"ds" => ["2008-01-13", "2009-01-03", "2010-01-16",
"2010-01-24", "2010-02-07", "2011-01-08",
"2013-01-12", "2014-01-12", "2014-01-19",
"2014-02-02", "2015-01-11", "2016-01-17",
"2016-01-24", "2016-02-07"],
"lower_window" => [0] * 14,
"upper_window" => [1] * 14
)
-superbowls = Daru::DataFrame.new(
+superbowls = Rover::DataFrame.new(
"holiday" => ["superbowl"] * 3,
"ds" => ["2010-02-07", "2014-02-02", "2016-02-07"],
"lower_window" => [0] * 3,
"upper_window" => [1] * 3
)
@@ -206,11 +206,11 @@
## Multiplicative Seasonality
[Explanation](https://facebook.github.io/prophet/docs/multiplicative_seasonality.html)
```ruby
-df = Daru::DataFrame.from_csv("example_air_passengers.csv")
+df = Rover.read_csv("example_air_passengers.csv")
m = Prophet.new(seasonality_mode: "multiplicative")
m.fit(df)
future = m.make_future_dataframe(periods: 50, freq: "MS")
forecast = m.predict(future)
```
@@ -234,10 +234,10 @@
[Explanation](https://facebook.github.io/prophet/docs/non-daily_data.html)
Sub-daily data
```ruby
-df = Daru::DataFrame.from_csv("example_yosemite_temps.csv")
+df = Rover.read_csv("example_yosemite_temps.csv")
m = Prophet.new(changepoint_prior_scale: 0.01).fit(df)
future = m.make_future_dataframe(periods: 300, freq: "H")
forecast = m.predict(future)
```