ACG2vec (Anime Comics Games to vector) are committed to creating a playground that combines ACG and Deep learning.(.
Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
R
Category
Model Development
Subcategory
Classical ML
Keywords
Timeseries
Stars
584
Forks
86
Contributors
11
Created date
2020-04-17
Updated date
2026-09-05
Stars in 1 day
0
Stars in 7 days
0
Rank
16,570
Previous rank
16,356
Rank change
-214
7-day stars rank
15,602
Previous 7-day stars rank
15,613
7-day stars rank change
11
Total stars rank
17,149
Previous total stars rank
17,118
Total stars rank change
-31
Country
USA
README summary
modeltime coverage]( Tidy time series forecasting in . 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 that walks you through the Modeltime Workflow. (Click to Watch on YouTube) Tutorials **Getting Started with Modeltime**: A walkthrough of the 6-Step Process for
using to forecast **Modeltime Documentation**: Learn how to **use** , **find** *Modeltime Models*, and **extend** so you can use new algorithms inside the *Modeltime Workflow*. Installation CRAN version: Development version: Why modeltime? Modeltime unlocks time series models and machine learning in one framework No need to switch back and forth between various frameworks. unlocks machine learning & classical time series analysis. **forecast**: Use ARIMA, ETS, and more models coming ( , , & ). **prophet**: Use Facebook’s Prophet algorithm ( & ) **tidymodels**: Use any model: , , , , to forecast Forecast
faster A streamlined workflow for forecasting Modeltime incorporates a streamlined workflow (see Getting Started with Modeltime) for using best practices to forecast. A streamlined workflow for forecasting Meet the modeltime ecosystem Learn a growing ecosystem of forecasting packages The modeltime ecosystem is growing Modeltime is part of a **growing ecosystem** of Modeltime forecasting packages. Modeltime (Machine Learning) Modeltime H2O (AutoML) Modeltime GluonTS (Deep Learning) Modeltime Ensemble (Blending Forecasts) Modeltime Resample (Backtesting) Timetk (Feature Engineering, Data Wrangling, Time Series Visualization) 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* 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**. You will learn: **Time Series Machine Learning** (cutting-edge) with - 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more) **Deep Learning** with (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
. Take the High-Performance Time Series Forecasting Course
Frequently asked questions
How many stars and forks are recorded?
business-science/modeltime has 584 stars and 86 forks.
What is the repository rank?
business-science/modeltime is ranked 16,570.
What language and category are recorded?
business-science/modeltime has 584 stars, uses R and belongs to Model Development.
Which repository is closest in rank?
business-science/modeltime is compared with abides-sim/abides, ranked 16,567 with 584 stars.
README
modeltime
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 that walks you
through the Modeltime Workflow.
Modeltime
Documentation: Learn
how to usemodeltime, findModeltime Models, and
extendmodeltime so you can use new algorithms inside the
Modeltime Workflow.
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