132 lines
No EOL
4.4 KiB
TeX
132 lines
No EOL
4.4 KiB
TeX
% Abbreviations for technical terms.
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\newglossaryentry{cart}{
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name=CART, description={Classification and Regression Trees}
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}
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\newglossaryentry{cv}{
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name=CV, description={Cross Validation}
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}
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\newglossaryentry{mase}{
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name=MASE, description={Mean Absolute Scaled Error}
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}
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\newglossaryentry{ml}{
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name=ML, description={Machine Learning}
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}
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\newglossaryentry{rf}{
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name=RF, description={Random Forest}
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}
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\newglossaryentry{stl}{
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name=STL, description={Seasonal and Trend Decomposition using Loess}
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}
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\newglossaryentry{svm}{
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name=SVM, description={Support Vector Machine}
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}
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\newglossaryentry{svr}{
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name=SVR, description={Support Vector Regression}
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}
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\newglossaryentry{udp}{
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name=UDP, description={Urban Delivery Platform}
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}
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\newglossaryentry{vrp}{
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name=VRP, description={Vehicle Routing Problem}
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}
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% Model names.
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\newglossaryentry{naive}{
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name=naive, description={(Seasonal) Na\"{i}ve Method}
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}
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\newglossaryentry{fnaive}{
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name=fnaive, description={"Flexible" STL Decomposition,
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with tuned ns parameter}
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}
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\newglossaryentry{pnaive}{
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name=pnaive, description={"Periodic" STL Decomposition,
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with ns parameter set to large number}
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}
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\newglossaryentry{trivial}{
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name=trivial, description={Trivial Method}
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}
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\newglossaryentry{hcroston}{
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name=hcroston, description={Croston's Method,
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trained on horizontal time series}
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}
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\newglossaryentry{hholt}{
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name=hholt, description={Holt's Linear Trend Method,
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trained on horizontal time series}
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}
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\newglossaryentry{vholt}{
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name=vholt, description={Holt's Linear Trend Method,
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trained on vertical time series}
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}
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\newglossaryentry{rtholt}{
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name=rtholt, description={Holt's Linear Trend Method,
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(re)trained on vertical time series}
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}
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\newglossaryentry{hhwinters}{
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name=hhwinters, description={Holt-Winter's Seasonal Method,
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trained on horizontal time series}
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}
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\newglossaryentry{hses}{
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name=hses, description={Simple Exponential Smoothing Method,
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trained on horizontal time series}
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}
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\newglossaryentry{vses}{
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name=vses, description={Simple Exponential Smoothing Method,
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trained on vertical time series}
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}
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\newglossaryentry{rtses}{
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name=rtses, description={Simple Exponential Smoothing Method,
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(re)trained on vertical time series}
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}
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\newglossaryentry{hsma}{
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name=hsma, description={Simple Moving Average Method,
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trained on horizontal time series}
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}
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\newglossaryentry{htheta}{
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name=htheta, description={Theta Method,
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trained on horizontal time series}
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}
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\newglossaryentry{vtheta}{
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name=vtheta, description={Theta Method,
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trained on vertical time series}
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}
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\newglossaryentry{rttheta}{
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name=rttheta, description={Theta Method,
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(re)trained on vertical time series}
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}
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\newglossaryentry{hets}{
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name=hets, description={ETS State Space Method,
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trained on horizontal time series}
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}
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\newglossaryentry{vets}{
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name=vets, description={ETS State Space Method,
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trained on vertical time series}
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}
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\newglossaryentry{rtets}{
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name=rtets, description={ETS State Space Method,
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(re)trained on vertical time series}
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}
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\newglossaryentry{harima}{
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name=harima, description={Autoregressive Integrated Moving Average
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Method,
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trained on horizontal time series}
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}
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\newglossaryentry{varima}{
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name=varima, description={Autoregressive Integrated Moving Average
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Method,
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trained on vertical time series}
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}
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\newglossaryentry{rtarima}{
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name=rtarima, description={Autoregressive Integrated Moving Average
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Method,
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(re)trained on vertical time series}
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}
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\newglossaryentry{vrfr}{
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name=vrfr, description={Random Forest Regression Method,
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trained on vertical time series}
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}
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\newglossaryentry{vsvr}{
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name=vsvr, description={Support Vector Regression Method,
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trained on vertical time series}
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}
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\printglossaries |