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 6 months ago
correlationexploratory-analysisexploratory-data-analysisexploratory-data-visualizationstidyverse
130 stars 4.59 score 102 dependencies
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 6 months ago
accuracy-metricsbacktestingbootstrapbootstrappingcross-validationforecastingmodeltimemodeltime-resampleresamplingstatisticstidymodelstime-series
17 stars 2.03 score 189 dependencies
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 6 months ago
broomforecastforecasting-modelspredictiontidytidyversetimetime-seriestimeseries
154 stars 5.04 score 131 dependencies 1 dependents
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 6 months ago
arimadata-sciencedeep-learningetsforecastingmachine-learningmachine-learning-algorithmsmodeltimeprophettbatstidymodelingtidymodelstimetime-seriestime-series-analysistimeseriestimeseries-forecasting
506 stars 7.51 score 187 dependencies 5 dependents
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 6 months ago
dplyrfinancial-analysisfinancial-datafinancial-statementsmultiple-stocksperformance-analysisperformanceanalyticsquantmodstockstock-exchangesstock-indexesstock-listsstock-performancestock-pricesstock-symboltidyversetime-seriestimeseriesxts
837 stars 8.68 score 133 dependencies
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 6 months ago
coercioncoercion-functionsdata-miningdplyrforecastforecastingforecasting-modelsmachine-learningseries-decompositionseries-signaturetibbletidytidyquanttidyversetimetime-seriestimeseries
601 stars 8.11 score 128 dependencies 14 dependents
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 6 months ago
anomalyanomaly-detectiondecompositiondetect-anomaliesiqrtime-series
337 stars 6.50 score 134 dependenciesalphavantager - 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 1 years ago
alpha-vantagefinancial-datahistorical-financial-data
69 stars 3.79 score 129 dependenciestibbletime - 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 1 years ago
periodicitytibbletimetime-seriestimeseries
180 stars 5.31 score 25 dependencies 4 dependentsriingo - 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
50 stars 3.32 score 21 dependencies