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See the e-mail I am now sending to the Data-SIG, because the issue probably requires internal rather than public discussion.
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The problem: we have a *lot* of benchmark functions, with more to come. Until we've been running for a while we don't want to wantonly disable benchmarks. So, we need to find a way to surface individu…
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when i use the following code, it will propose an exception: normal_lpdf: Scale parameter is 0, but must be >0!
# Python
m = Prophet(mcmc_samples=500)
forecast = m.fit(df).predict(future)
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As per subject; taking out `poly()` term avoids the error.
``` r
library(fasster)
library(tsibble)
library(lubridate)
daytype %
mutate(DayType = daytype(Date)) %>%
as_tsibble()
…
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Predicting stock prices using Technical Analysis is a 16th century Japanese tech.
Let's improve it by creating an Trading Advisor based on **Machine Learning** who can predict trends.
Using **senti…
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Pardon me in advance for being new to Prophet (and forecasting in general). I am trying to fit a curve for approximately 2 years worth of datetime data [posted here](http://bit.ly/2po0xPJ), and foreca…
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G'day
Thanks again for open-sourcing fbprophet.
I wanted to do some testing of the library using synthetic data, before using it on real data. Attached to this issue is a zipped Jupyter notebook…
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I really like the brms package as it has very nice set of models without a need to manual write the stan code. I'd like to use also bsts models as part of that but currently they are limited to simple…
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Purpose of this task is to test and recommend methods that can handle hourly data and yield hourly results.
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Buildings with usage patterns that are not correctly captured using Caltrack models end up with low signal-to-noise ratio, poor model fits and tend to default to the intercept-only model. These buildi…