Please see my markdown hosted on github which shows some surprising results regarding investments based on forecasts and uncorrelated data. It turns out that uncorrelated data can yield better returns.
Over at github I have put the following:
This introduces a few known and a few new forecast functions. It then builds an ensemble forecast out of 13 models. It has the following steps:
- Learn all models over training period
- Predict h periods ahead and build a weighted Bayesian model of the forecasts
- Retrain the model on training + h to give new forecasts beyond this period (using previous weights)
It introduces four Bayesian models in stan
- ARMA(2, 1)
- ARMA(2, 1) with weighting of obs
- Local linear trend
- Weight model (eg it can model 13 weights on 13 X variables and 10 time steps which is not possible in frequentist setup)
Note that most code has tests around the functions. You need to load all scripts to get the
forecastEns() to run.
Many GCSE results are reported in a very compact form. I have written some R code which via simulation allows to translate grade brackets into numeric grades. You can read it here. I specifically look at grades and dispersion by gender.
Please see this blog post.
There has been a lot of criticism that many modern algorithms enforce inequality and are racist. This example shows how a predictive model can be made less discriminatory.