Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Pycaret | 7,076 | 13 | 20 hours ago | 83 | June 06, 2022 | 261 | mit | Jupyter Notebook | ||
An open-source, low-code machine learning library in Python | ||||||||||
Sktime | 6,284 | 2 days ago | 678 | bsd-3-clause | Python | |||||
A unified framework for machine learning with time series | ||||||||||
Darts | 5,579 | 7 | a day ago | 25 | June 22, 2022 | 214 | apache-2.0 | Python | ||
A python library for user-friendly forecasting and anomaly detection on time series. | ||||||||||
Autogluon | 5,498 | a day ago | 220 | apache-2.0 | Python | |||||
AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data | ||||||||||
Data Science | 3,528 | 5 days ago | 4 | Jupyter Notebook | ||||||
Collection of useful data science topics along with articles, videos, and code | ||||||||||
Gluonts | 3,423 | 7 | 19 hours ago | 58 | June 30, 2022 | 347 | apache-2.0 | Python | ||
Probabilistic time series modeling in Python | ||||||||||
Tsai | 3,226 | 1 | 19 hours ago | 41 | April 19, 2022 | 21 | apache-2.0 | Jupyter Notebook | ||
Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai | ||||||||||
Merlion | 2,921 | 3 days ago | 14 | June 28, 2022 | 14 | bsd-3-clause | Python | |||
Merlion: A Machine Learning Framework for Time Series Intelligence | ||||||||||
Neural_prophet | 2,848 | 3 days ago | 7 | March 22, 2022 | 96 | mit | Python | |||
NeuralProphet: A simple forecasting package | ||||||||||
Pytorch Forecasting | 2,666 | 4 | 12 days ago | 33 | May 23, 2022 | 359 | mit | Python | ||
Time series forecasting with PyTorch |
GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models, based on PyTorch and MXNet.
GluonTS requires Python 3.7 or newer, and the easiest way to install it is via pip
:
# support for mxnet models, faster datasets
pip install "gluonts[mxnet,pro]"
# support for torch models, faster datasets
pip install "gluonts[torch,pro]"
To illustrate how to use GluonTS, we train a DeepAR-model and make predictions using the simple "airpassengers" dataset. The dataset consists of a single time series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months). We split the dataset into train and test parts, by removing the last three years (36 month) from the train data. Thus, we will train a model on just the first nine years of data.
import pandas as pd
import matplotlib.pyplot as plt
from gluonts.dataset.pandas import PandasDataset
from gluonts.dataset.split import split
from gluonts.mx import DeepAREstimator, Trainer
# Load data from a CSV file into a PandasDataset
df = pd.read_csv(
"https://raw.githubusercontent.com/AileenNielsen/"
"TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv",
index_col=0,
parse_dates=True,
)
dataset = PandasDataset(df, target="#Passengers")
# Train a DeepAR model on all data but the last 36 months
training_data, test_gen = split(dataset, offset=-36)
model = DeepAREstimator(
prediction_length=12, freq="M", trainer=Trainer(epochs=5)
).train(training_data)
# Generate test instances and predictions for them
test_data = test_gen.generate_instances(prediction_length=12, windows=3)
forecasts = list(model.predict(test_data.input))
# Plot predictions
df["#Passengers"].plot(color="black")
for forecast, color in zip(forecasts, ["green", "blue", "purple"]):
forecast.plot(color=f"tab:{color}")
plt.legend(["True values"], loc="upper left", fontsize="xx-large")
Note that the forecasts are displayed in terms of a probability distribution: The shaded areas represent the 50% and 90% prediction intervals, respectively, centered around the median.
If you wish to contribute to the project, please refer to our contribution guidelines.
If you use GluonTS in a scientific publication, we encourage you to add the following references to the related papers, in addition to any model-specific references that are relevant for your work:
@article{gluonts_jmlr,
author = {Alexander Alexandrov and Konstantinos Benidis and Michael Bohlke-Schneider
and Valentin Flunkert and Jan Gasthaus and Tim Januschowski and Danielle C. Maddix
and Syama Rangapuram and David Salinas and Jasper Schulz and Lorenzo Stella and
Ali Caner Trkmen and Yuyang Wang},
title = {{GluonTS: Probabilistic and Neural Time Series Modeling in Python}},
journal = {Journal of Machine Learning Research},
year = {2020},
volume = {21},
number = {116},
pages = {1-6},
url = {http://jmlr.org/papers/v21/19-820.html}
}
@article{gluonts_arxiv,
author = {Alexandrov, A. and Benidis, K. and Bohlke-Schneider, M. and
Flunkert, V. and Gasthaus, J. and Januschowski, T. and Maddix, D. C.
and Rangapuram, S. and Salinas, D. and Schulz, J. and Stella, L. and
Trkmen, A. C. and Wang, Y.},
title = {{GluonTS: Probabilistic Time Series Modeling in Python}},
journal = {arXiv preprint arXiv:1906.05264},
year = {2019}
}