timetk - A Tool Kit for Working with Time Series

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'.

Last updated 1 years ago

coercioncoercion-functionsdata-miningdplyrforecastforecastingforecasting-modelsmachine-learningseries-decompositionseries-signaturetibbletidytidyquanttidyversetimetime-seriestimeseries

14.42 score 615 stars 16 dependents 4.0k scripts 15k downloads

tidyquant - Tidy Quantitative Financial Analysis

Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' and 'PerformanceAnalytics' package functions and returns the objects in the tidy 'tibble' format. The main advantage is being able to use quantitative functions with the 'tidyverse' functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See the 'tidyquant' website for more information, documentation and examples.

Last updated 14 days ago

dplyrfinancial-analysisfinancial-datafinancial-statementsmultiple-stocksperformance-analysisperformanceanalyticsquantmodstockstock-exchangesstock-indexesstock-listsstock-performancestock-pricesstock-symboltidyversetime-seriestimeseriesxts

13.02 score 861 stars 4.9k scripts 15k downloads

tibbletime - Time Aware Tibbles

Built on top of the 'tibble' package, 'tibbletime' is an extension that allows for the creation of time aware tibbles. Some immediate advantages of this include: the ability to perform time-based subsetting on tibbles, quickly summarising and aggregating results by time periods, and creating columns that can be used as 'dplyr' time-based groups.

Last updated 2 months ago

periodicitytibbletimetime-seriestimeseriescpp

10.47 score 178 stars 2 dependents 644 scripts 5.3k downloads

modeltime - The Tidymodels Extension for Time Series Modeling

The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (<https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).

Last updated 3 months ago

arimadata-sciencedeep-learningetsforecastingmachine-learningmachine-learning-algorithmsmodeltimeprophettbatstidymodelingtidymodelstimetime-seriestime-series-analysistimeseriestimeseries-forecasting

10.44 score 547 stars 7 dependents 1.1k scripts 2.2k downloads

sweep - Tidy Tools for Forecasting

Tidies up the forecasting modeling and prediction work flow, extends the 'broom' package with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions for various forecasting models, and enables converting 'forecast' objects to "tidy" data frames with 'sw_sweep'.

Last updated 1 years ago

broomforecastforecasting-modelspredictiontidytidyversetimetime-seriestimeseries

9.61 score 155 stars 1 dependents 403 scripts 2.4k downloads

anomalize - Tidy Anomaly Detection

The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it's quite simple to decompose time series, detect anomalies, and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). Time series decomposition is used to remove trend and seasonal components via the time_decompose() function and methods include seasonal decomposition of time series by Loess ("stl") and seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements two methods for anomaly detection of residuals including using an inner quartile range ("iqr") and generalized extreme studentized deviation ("gesd"). These methods are based on those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. Refer to the associated functions for specific references for these methods.

Last updated 1 years ago

anomalyanomaly-detectiondecompositiondetect-anomaliesiqrtime-series

9.53 score 339 stars 268 scripts 2.5k downloads

modeltime.ensemble - Ensemble Algorithms for Time Series Forecasting with Modeltime

A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability.

Last updated 6 months ago

ensembleensemble-learningforecastforecastingmodeltimestackingstacking-ensembletidymodelstimetime-seriestimeseries

8.29 score 75 stars 143 scripts 436 downloads

correlationfunnel - Speed Up Exploratory Data Analysis (EDA) with the Correlation Funnel

Speeds up exploratory data analysis (EDA) by providing a succinct workflow and interactive visualization tools for understanding which features have relationships to target (response). Uses binary correlation analysis to determine relationship. Default correlation method is the Pearson method. Lian Duan, W Nick Street, Yanchi Liu, Songhua Xu, and Brook Wu (2014) <doi:10.1145/2637484>.

Last updated 1 years ago

correlationexploratory-analysisexploratory-data-analysisexploratory-data-visualizationstidyverse

7.20 score 137 stars 117 scripts 356 downloads

modeltime.resample - Resampling Tools for Time Series Forecasting

A 'modeltime' extension that implements forecast resampling tools that assess time-based model performance and stability for a single time series, panel data, and cross-sectional time series analysis.

Last updated 1 years ago

accuracy-metricsbacktestingbootstrapbootstrappingcross-validationforecastingmodeltimemodeltime-resampleresamplingstatisticstidymodelstime-series

6.64 score 19 stars 1 dependents 38 scripts 470 downloads

alphavantager - Lightweight Interface to the Alpha Vantage API

Alpha Vantage has free historical financial information. All you need to do is get a free API key at <https://www.alphavantage.co>. Then you can use the R interface to retrieve free equity information. Refer to the Alpha Vantage website for more information.

Last updated 2 years ago

alpha-vantagefinancial-datahistorical-financial-data

5.90 score 68 stars 65 scripts 1.8k downloads

riingo - An R Interface to the 'Tiingo' Stock Price API

Functionality to download stock prices, cryptocurrency data, and more from the 'Tiingo' API <https://api.tiingo.com/>.

Last updated 4 years ago

5.27 score 52 stars 51 scripts 1.4k downloads