can someone explain what's the meaning of this line > df['y'] = np.log(df['y'])
df is a dataframe, which is like a spreadsheet. This line takes the logarithm of the column named 'y' and updates it in place.
Prophet: forecasting at scale
111–114 of 114 posts
Re: Prophet: forecasting at scale
#112Earlier quoted context omitted.
Bought way back in August 1929: Annual Total Return: 9.1% Annual Real Total Return: 5.9% Bought in January 1987, held for a realistic 30 years: Annual Total Return: 9.8% Annual Real Total Return: 7.0% There's always going to be some deviation, but over any given multi-decade holding period, you will generally end up with a predictable 5-9% annualized (inflation-adjusted) return. That is more than zero. My point stand…
You conveniently ignored half the problem by buying in 1929 and holding for 88 years, which is reasonable if you are currently about 140 years old. If not, look at http://www.macrotrends.net/1319/dow-jones-100-year-historica... Let's buy in August 1929 at 5338.69, and sell 20 years later, in August 1949, at 1822.87 (inflation-adjusted). Congratulations, you lost two thirds of your money. Sell 30 years later instead?…
Re: Prophet: forecasting at scale
#113Has anyone managed to get this working on windows with Juypter (Anaconda build) struggling with Pystan errors. Any guidance welcomed.
Re: Prophet: forecasting at scale
#114Earlier quoted context omitted.
It doesn't assume the Effiecient Market hypothesis - empirical studies of returns support random returns without the imposing a model (non-parametric tests). That's not to say returns are actually random, but in any given time range, it appears to be.
EMH and random walk theory are intrinsically linked; you can't have one without the other... Or are you saying that movements aren't actually random, and only appear to be?