wfm

There are 0 repository under wfm topic.

• reald / sdr-collection

  SDR collection

  sdrgnuradiognuradio-companionvorfmradioreceivervhfomnidirectionalrangewfmssbusblsbdopplersatellitesatellite-trackingfrequency
  14

• oxidecomputer / wfm-to-pcap

  .wfm to .pcap decoder

  8b10bwfmqsgmiisgmii
  Language:Rust 7

• vongostev / TekWFM2

  Parser for Tektronix WFM v.2 binary files

  tektronixwfmparseroscilloscope
  Language:Python 4

• feedhenry-raincatcher / raincatcher-angularjs

  :tada: Raincatcher WFM, Angularjs and Apache Cordova based framework

  angularwfmraincatcherdemo-apps
  Language:HTML 3

• tesfahunboshe / Workforce-Management-App-with-Shiny

  This app was developed using Shiny package. It can be used for workforce management purposes for small and medium size call centers of similar purposes. The app has a page for schedule view, another for the coverage view per market and per channel, and another two pages for submitting preferences and viewing team schedule for the selected day range.

  schedulershinywfmscheduleworkforce-managementdata-scienceshinyappsanalytics
  Language:R 2

• nosoxon / libtekwfm3

  Full (ALL members included) Tektronix WFM v3 C headers and Python bindings

  fastframetektronixwaveformoscilloscopewfm
  Language:C 0

• Pearson-ICC / CallForecast

  cxengagewfm
  Language:Python 0

• tesfahunboshe / WFM-forecasting-with-R

  Forecasting is the first important stage of workforce management planning. WFM forecastors create the forecast of ,including others, volume of conversations to be expected in some future time. Forecasting future contacts is a difficult task because of the uncertainty associated with the many factors that determine contacts as well as the nature of distribution of contacts in time. Time series data is an object observed in many consecutive units of time in chronological order. While there is not a single best method for forecasting, there are widely used methods and best practices. We will discuss some of the most commonly used time series models and hybrid approaches in this blog. We will also cover the metrics which can indicate the accuracy of the forecast. accuracy() function from forecast package gives mean error (ME), root mean square error (RMSE), mean absolute error (MAE), mean percentage error (MPE), mean absolute percentage (MAPE), mean absolute squared error (MASE), autocorrelation function index (ACFI) and Theil’s U values. MAPE is considered the best accuracy measure since it is not sensitive to sign of error and the magnitude of units. We will use MAPE measures to compare the accuracy of different methods to be discussed. The html output is currently available at rPubs: https://rpubs.com/Tesfahun_Boshe/873021

  forecastingwfmworkforcemanagement-wtime-seriesr