From eabd4c8b9ed7326f578895fe105ce6895153a79a Mon Sep 17 00:00:00 2001 From: michellegrushkometa <113440684+michellegrushkometa@users.noreply.github.com> Date: Thu, 15 Dec 2022 10:04:16 -0500 Subject: [PATCH] docs: added time series validation to features section of website (#565) LGTM. Thanks @michellegrushkometa and congrats for your first official PR merged to main branch! <3 --- website/docs/features2.mdx | 11 ++++++++++- .../img/actual_vs_predicted_response_ts.png | Bin 0 -> 158178 bytes ...time_series_validation_and_convergence.png | Bin 0 -> 2660263 bytes 3 files changed, 10 insertions(+), 1 deletion(-) create mode 100644 website/static/img/actual_vs_predicted_response_ts.png create mode 100644 website/static/img/time_series_validation_and_convergence.png diff --git a/website/docs/features2.mdx b/website/docs/features2.mdx index 912f443f8..35c8cab97 100644 --- a/website/docs/features2.mdx +++ b/website/docs/features2.mdx @@ -230,7 +230,6 @@ Ridge regression has an additional benefit of being relatively easy to interpret ## Multi-Objective Hyperparameter Optimization with Nevergrad ---- One of the most important innovation in Robyn is the implementation of multi-objective hyperparameter optimization in MMM. This enables us to automate the selection of adstocking, saturation, regularization penalty and even training size for time-series validation. At the same time, the ability to optimise towards multiple "goals", implemented as objective functions, provides us the edge to produce model candidates with great predictive power as well as more realistic effect decomposition. @@ -281,6 +280,16 @@ To investigate the convergence of your multi-objective optimization. See below f moo distrb plot moo cloud plot +--- +## Time Series Validation + +When `ts_validation = TRUE` in `robyn_run()` a 3-way-split time series for NRMSE validation is enabled. +A time series validation parameter `train_size` is included as one of Robyn's hyperparameters. When `ts_validation = TRUE` in `robyn_run()`, `train_size` defines the percentage of data used for training, validation and out-of-sample testing. For example, when `train_size = 0.7`, `val_size` and `test_size` will be 0.15 each. This hyperparameter is customizable or can be fixed with a default range of `c(0.5, 0.8)` and must be between `c(0.1, 1)`. + +time series validation + +time series validation + --- ## Calibration with Experiments diff --git a/website/static/img/actual_vs_predicted_response_ts.png b/website/static/img/actual_vs_predicted_response_ts.png new file mode 100644 index 0000000000000000000000000000000000000000..fd7841f50c0affd2eedf2cf76986e1757d851eb7 GIT binary patch literal 158178 zcmdSBWmuGL*fk2GU=Y#`3Mfc-hcSSNFd_`ifOL1aqEZ49(l7`pICOVOgLHSdLpQ_l zUHH7;alAkGv48D7KVTSU?)%E~T<2Qrn&3Ce@&tEj?qXqK5h%Qbyv4%01)kohy?q1x zl6ms!0Tvdvs>O>JZ!C;#u&}7U85tNzMn)&={9$Qq{F_~obZ7w$w&Gd>ZByge;OO(!T-O04R29?ZukOq1ABDvfg7uFU`(cZjK8^=84m6n zs;3Z{K>y1&Hdm5|u?_z3n-)oNaGYeGb6U{gG#@MD;s)#7=KFRFA0M5Ek3SX85=Zgv zS>Ax5fkF1W(u}9@0E`YWR}(&ho40+)2hf0tXjM^x;dZ zzj3P`c}Ouzx^tG8^(Bj$$*oDP{Sk{oB5#7329N%^4Q^NL|DI!U-@MuQ-&5WP53q3m zJ@v=N#^(L+Mf}^h{r`Kb!PBQ$`2W3#bK{1=f6u7y-^arF_q5U9AB*b07jgf;d`W}K 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