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https://github.com/business-science/modeltime
Modeltime unlocks time series forecast models and machine learning in one framework
https://github.com/business-science/modeltime
arima data-science deep-learning ets forecasting machine-learning machine-learning-algorithms modeltime prophet r-package tbats tidymodeling tidymodels time time-series time-series-analysis timeseries timeseries-forecasting
Last synced: about 11 hours ago
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Modeltime unlocks time series forecast models and machine learning in one framework
- Host: GitHub
- URL: https://github.com/business-science/modeltime
- Owner: business-science
- License: other
- Created: 2020-04-17T13:00:41.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-01-04T19:44:05.000Z (about 1 year ago)
- Last Synced: 2024-04-23T19:24:36.557Z (9 months ago)
- Topics: arima, data-science, deep-learning, ets, forecasting, machine-learning, machine-learning-algorithms, modeltime, prophet, r-package, tbats, tidymodeling, tidymodels, time, time-series, time-series-analysis, timeseries, timeseries-forecasting
- Language: R
- Homepage: https://business-science.github.io/modeltime/
- Size: 50.9 MB
- Stars: 499
- Watchers: 28
- Forks: 81
- Open Issues: 55
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
- awesome-time-series - modeltime
README
---
output: github_document
---
```{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
[](https://cran.r-project.org/package=modeltime)
[]( https://app.codecov.io/gh/business-science/modeltime?branch=master)
[](https://github.com/business-science/modeltime/actions)
> 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.
## 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.
```{r, echo=F, out.width='100%', fig.align='center', fig.cap="A streamlined workflow for forecasting"}
knitr::include_graphics("vignettes/modeltime_workflow.jpg")
```
## 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
[_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.
Become the Time Series Expert for your organization.
