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README.Rmd
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README.Rmd
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---
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://app.codecov.io/gh/business-science/modeltime?branch=master)
[](https://github.com/business-science/modeltime/actions)
<!-- badges: end -->
> Tidy time series forecasting in `R`.
Mission: Our number 1 goal is to make high-performance time series analysis easier, faster, and more scalable. Modeltime solves this with a simple to use infrastructure for modeling and forecasting time series.
## 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>