Displaying 1 to 20 from 29 results

Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth

  •    Python

Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers.Prophet is open source software released by Facebook's Core Data Science team. It is available for download on CRAN and PyPI.

Merlion - Merlion: A Machine Learning Framework for Time Series Intelligence

  •    Python

Merlion is a Python library for time series intelligence. It provides an end-to-end machine learning framework that includes loading and transforming data, building and training models, post-processing model outputs, and evaluating model performance. It supports various time series learning tasks, including forecasting and anomaly detection for both univariate and multivariate time series. This library aims to provide engineers and researchers a one-stop solution to rapidly develop models for their specific time series needs, and benchmark them across multiple time series datasets. The table below provides a visual overview of how Merlion's key features compare to other libraries for time series anomaly detection and/or forecasting.




forecast - forecast package for R

  •    R

The R package forecast provides methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling. You can install the stable version from CRAN.

gluon-ts - GluonTS - Probabilistic Time Series Modeling in Python

  •    Python

GluonTS is a Python toolkit for probabilistic time series modeling, built around Apache MXNet (incubating). GluonTS provides utilities for loading and iterating over time series datasets, state of the art models ready to be trained, and building blocks to define your own models and quickly experiment with different solutions.

orbit - A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood

  •    Python

Orbit is a Python package for Bayesian time series forecasting and inference. It provides a familiar and intuitive initialize-fit-predict interface for time series tasks, while utilizing probabilistic programming languages under the hood.

atspy - AtsPy: Automated Time Series Models in Python (by @firmai)

  •    Python

Animated investment research at Sov.ai, sponsoring open source initiatives. Easily develop state of the art time series models to forecast univariate data series. Simply load your data and select which models you want to test. This is the largest repository of automated structural and machine learning time series models. Please get in contact if you want to contribute a model. This is a fledgling project, all advice appreciated.


web-traffic-forecasting - Kaggle | Web Traffic Forecasting 📈

  •    Python

My solution for the Web Traffic Forecasting competition hosted on Kaggle. The training dataset consists of approximately 145k time series. Each of these time series represents a number of daily views of a different Wikipedia article, starting from July 1st, 2015 up until September 10th, 2017. The goal is to forecast the daily views between September 13th, 2017 and November 13th, 2017 for each article in the dataset. The name of the article as well as the type of traffic (all, mobile, desktop, spider) is given for each article.

StockPricePrediction - Stock Price Prediction using Machine Learning Techniques

  •    Jupyter

To examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical news indicators to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data. Download the Dataset needed for running the code from here.

demography - demography package for R

  •    R

The R package demography provides functions for demographic analysis including: lifetable calculations; Lee-Carter modelling; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting. You can install the stable version on R CRAN.

fpp2-package - All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos

  •    R

The R package fpp2 contains all data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos (http://OTexts.org/fpp2/). All packages required to run the examples are also loaded. You can install the stable version on R CRAN.

awips2 - Weather forecasting display and analysis package developed by NWS/Raytheon, released as open source software by Unidata

  •    Java

AWIPS (formerly know as AWIPS II or AWIPS2) is a meteorological display and analysis package developed by the National Weather Service and Raytheon for operational forecasting. AWIPS is a Java application consisting of a data-rendering client (CAVE, which runs on Red Hat/CentOS Linux, macOS, and Windows), and a backend data server (EDEX, which runs on x86_64 Red Hat/CentOS 6 and 7). Unidata AWIPS source code and binaries (RPMs) are considered to be in the public domain, meaning there are no restrictions on any download, modification, or distribution in any form (original or modified). Unidata AWIPS contains no proprietery content and is therefore not subject to export controls as stated in the Master Rights licensing file.

python-awips - Python Data Access Framework to request AWIPS data from an EDEX server.

  •    Python

Unidata AWIPS source code and binaries (RPMs) are considered to be in the public domain, meaning there are no restrictions on any download, modification, or distribution in any form (original or modified). The Python AWIPS package contains no proprietery content and is therefore not subject to export controls as stated in the Master Rights licensing file and source code headers.

maltese - Little R utility package for making time series data more machine learning-friendly

  •    R

The included dataset is a tidy time series of pageviews for R's article on English Wikipedia from 2015-10-01 to 2017-01-30. See the vignette for a detailed walkthrough.

Mcomp - Data from the M-competitions

  •    R

The R package Mcomp contains the 1001 time series from the M-competition (Makridakis et al. 1982) and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000). See also the tscompdata package. You can install the stable version on R CRAN.

TSstudio - Tools for time series analysis and forecasting

  •    R

The TSstudio package provides a set of interactive visualization tools for time series analysis and forecasting. The TSstudio package provides a set of tools for time series analysis, supporting “ts”, “mts”, “zoo”, and “xts” objects. It includes interactive visualizations tools based on the plotly package for descriptive time series analysis. In addition for the visualization tools, the package provides a set of utility functions for preprocessing time series objects.

timetk - A toolkit for working with time series in R

  •    R

An example of the forecasting capabilities as shown in vignette TK03 - Forecasting Using a Time Series Signature with timetk. Get an index: tk_index returns the time series index of time series objects, models. The argument timetk_idx can be used to return a special timetk "index" attribute for regularized ts objects that returns a non-regularized date / date-time index if present.

anompy - A Python library for anomaly detection

  •    Python

anompy is a Python package of forecasting and anomaly detection algorithms. See this notebook for more examples.

anticipy - A Python library for time series forecasting

  •    Python

Anticipy is a tool to generate forecasts for time series. It takes a pandas Series or DataFrame as input, and returns a DataFrame with the forecasted values for a given period of time.