forked from business-science/modeltime
-
Notifications
You must be signed in to change notification settings - Fork 0
/
README.Rmd
170 lines (107 loc) · 6.58 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
message = F,
warning = F,
dpi = 200
)
```
# modeltime
<!-- badges: start -->
[](https://cran.r-project.org/package=modeltime)
[](https://github.com/business-science/modeltime/actions)
[]( https://app.codecov.io/gh/business-science/modeltime?branch=master)
<!-- badges: end -->
> Tidy time series forecasting with `tidymodels`.
## Quickstart Video
For those that prefer video tutorials, we have an [11-minute YouTube Video](https://www.youtube.com/watch?v=-bCelif-ENY) that walks you through the Modeltime Workflow.
<a href="https://www.youtube.com/watch?v=-bCelif-ENY" target="_blank">
<p style='text-align:center;'>
<img src= "vignettes/modeltime-video.jpg"
alt="Introduction to Modeltime" width="60%"/>
</p>
<p style='text-align:center'>(Click to Watch on YouTube)</p>
</a>
## Tutorials
- [__Getting Started with Modeltime__](https://business-science.github.io/modeltime/articles/getting-started-with-modeltime.html): A walkthrough of the 6-Step Process for using `modeltime` to forecast
- [__Modeltime Documentation__](https://business-science.github.io/modeltime/): Learn how to __use__ `modeltime`, __find__ _Modeltime Models_, and __extend__ `modeltime` so you can use new algorithms inside the _Modeltime Workflow_.
## Installation
CRAN version:
``` r
install.packages("modeltime", dependencies = TRUE)
```
Development version:
``` r
remotes::install_github("business-science/modeltime", dependencies = TRUE)
```
## Why modeltime?
> Modeltime unlocks time series models and machine learning in one framework
```{r, echo=F, out.width='100%', fig.align='center'}
knitr::include_graphics("vignettes/forecast_plot.jpg")
```
No need to switch back and forth between various frameworks. `modeltime` unlocks machine learning & classical time series analysis.
- __forecast__: Use ARIMA, ETS, and more models coming (`arima_reg()`, `arima_boost()`, & `exp_smoothing()`).
- __prophet__: Use Facebook's Prophet algorithm (`prophet_reg()` & `prophet_boost()`)
- __tidymodels__: Use any `parsnip` model: `rand_forest()`, `boost_tree()`, `linear_reg()`, `mars()`, `svm_rbf()` to forecast
## Forecast faster
> A streamlined workflow for forecasting
Modeltime incorporates a [streamlined workflow (see Getting Started with Modeltime)](https://business-science.github.io/modeltime/articles/getting-started-with-modeltime.html) for using best practices to forecast.
<hr>
```{r, echo=F, out.width='100%', fig.align='center', fig.cap="A streamlined workflow for forecasting"}
knitr::include_graphics("vignettes/modeltime_workflow.jpg")
```
<hr>
## Meet the modeltime ecosystem
> Learn a growing ecosystem of forecasting packages
```{r, echo=F, out.width='100%', fig.align='center', fig.cap="The modeltime ecosystem is growing"}
knitr::include_graphics("man/figures/modeltime_ecosystem.jpg")
```
Modeltime is part of a __growing ecosystem__ of Modeltime forecasting packages.
- [Modeltime (Machine Learning)](https://business-science.github.io/modeltime/)
- [Modeltime H2O (AutoML)](https://business-science.github.io/modeltime.h2o/)
- [Modeltime GluonTS (Deep Learning)](https://business-science.github.io/modeltime.gluonts/)
- [Modeltime Ensemble (Blending Forecasts)](https://business-science.github.io/modeltime.ensemble/)
- [Modeltime Resample (Backtesting)](https://business-science.github.io/modeltime.resample/)
- [Timetk (Feature Engineering, Data Wrangling, Time Series Visualization)](https://business-science.github.io/timetk/)
## Summary
Modeltime is an amazing ecosystem for time series forecasting. But it can take a long time to learn:
- Many algorithms
- Ensembling and Resampling
- Machine Learning
- Deep Learning
- Scalable Modeling: 10,000+ time series
Your probably thinking how am I ever going to learn time series forecasting. Here's the solution that will save you years of struggling.
## Take the High-Performance Forecasting Course
> Become the forecasting expert for your organization
<a href="https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/" target="_blank"><img src="https://www.filepicker.io/api/file/bKyqVAi5Qi64sS05QYLk" alt="High-Performance Time Series Forecasting Course" width="100%" style="box-shadow: 0 0 5px 2px rgba(0, 0, 0, .5);"/></a>
[_High-Performance Time Series Course_](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/)
### Time Series is Changing
Time series is changing. __Businesses now need 10,000+ time series forecasts every day.__ This is what I call a _High-Performance Time Series Forecasting System (HPTSF)_ - Accurate, Robust, and Scalable Forecasting.
__High-Performance Forecasting Systems will save companies by improving accuracy and scalability.__ Imagine what will happen to your career if you can provide your organization a "High-Performance Time Series Forecasting System" (HPTSF System).
### How to Learn High-Performance Time Series Forecasting
I teach how to build a HPTFS System in my [__High-Performance Time Series Forecasting Course__](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting). You will learn:
- __Time Series Machine Learning__ (cutting-edge) with `Modeltime` - 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
- __Deep Learning__ with `GluonTS` (Competition Winners)
- __Time Series Preprocessing__, Noise Reduction, & Anomaly Detection
- __Feature engineering__ using lagged variables & external regressors
- __Hyperparameter Tuning__
- __Time series cross-validation__
- __Ensembling__ Multiple Machine Learning & Univariate Modeling Techniques (Competition Winner)
- __Scalable Forecasting__ - Forecast 1000+ time series in parallel
- and more.
<p class="text-center" style="font-size:24px;">
Become the Time Series Expert for your organization.
</p>
<br>
<p class="text-center" style="font-size:30px;">
<a href="https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting">Take the High-Performance Time Series Forecasting Course</a>
</p>