https://github.com/config-i1/smooth
The set of functions used for time series analysis and in forecasting.
https://github.com/config-i1/smooth
arima arima-forecasting ces ets exponential-smoothing forecast r-package state-space time-series
Last synced: 4 months ago
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The set of functions used for time series analysis and in forecasting.
- Host: GitHub
- URL: https://github.com/config-i1/smooth
- Owner: config-i1
- Created: 2016-01-25T12:06:26.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2025-03-30T19:33:22.000Z (about 1 year ago)
- Last Synced: 2025-03-30T20:25:50.563Z (about 1 year ago)
- Topics: arima, arima-forecasting, ces, ets, exponential-smoothing, forecast, r-package, state-space, time-series
- Language: R
- Homepage:
- Size: 9.26 MB
- Stars: 90
- Watchers: 5
- Forks: 21
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS
Awesome Lists containing this project
- awesome-time-series - smooth
README
# smooth
R:
[](https://cran.r-project.org/package=smooth)
[](https://cran.r-project.org/package=smooth)
[](https://github.com/config-i1/smooth/actions/workflows/test.yml)
Python:
The **smooth** package implements Single Source of Error (SSOE) state-space models for forecasting and time series analysis, available for both R and Python.
## Installation
**R (CRAN):**
```r
install.packages("smooth")
```
**R (github):**
```r
if (!require("remotes")) install.packages("remotes")
remotes::install_github("config-i1/smooth")
```
**Python (PyPI):**
```bash
# Not yet available
```
**Python (github):**
```bash
pip install "git+https://github.com/config-i1/smooth.git@master#subdirectory=python"
```
For development versions and system requirements, see the [Installation wiki page](https://github.com/config-i1/smooth/wiki/Installation).
## Quick Examples
### R
```r
library(smooth)
# ADAM - the recommended function for most tasks
model <- adam(y, model="ZXZ", lags=12)
forecast(model, h=12)
# Exponential Smoothing
model <- es(y, model="ZXZ", lags=12)
# Automatic model selection for ETS+ARIMA and distributions
model <- auto.adam(y, model="ZZZ",
orders=list(ar=2, i=2, ma=2, select=TRUE))
```
### Python
```python
from smooth import ADAM, ES
# ADAM model
model = ADAM(model="ZXZ", lags=12)
model.fit(y)
model.predict(h=12)
# Exponential Smoothing
model = ES(model="ZXZ")
model.fit(y)
```
## Documentation
Full documentation is available on the **[GitHub Wiki](https://github.com/config-i1/smooth/wiki)**, including:
- [ADAM](https://github.com/config-i1/smooth/wiki/ADAM) - Main unified ETS/ARIMA framework
- [Function reference](https://github.com/config-i1/smooth/wiki) - All functions and methods
- [Installation guide](https://github.com/config-i1/smooth/wiki/Installation) - Dependencies and troubleshooting
**Book:** Svetunkov, I. (2023). *Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM)*. Chapman and Hall/CRC. Online: https://openforecast.org/adam/