ddmd

> Discrete mathematics theory is possibly more difficult than the set of complex numbers.

Probably it is so because in continuous case there is the luxury of having enough points between any other points. In discrete case you are much more constrained, for example, you cannot choose a point between 0 and 1, and what is the length of the diagonal of a binary cube or angle between its two hyper-planes? Sometimes these notions can be naturally defined but in other cases the formal theory is not so natural.

By the way, an interesting question is how complex boolean numbers could be defined (naturally).

One interesting question is how to implement linear algebra for the case of binary variables x_i in {0,1} or x_i in {-1,0,+1} as opposed to having continuous variables. Of course, it will be somewhat different theory but the general properties should be analogous to those of standard linear algebra. For example, dot product should express the notion of orthogonality, there should be well defined distance etc. I am not sure that such a theory exists at all but if yes then I am curious if there exist implementations.

I am not sure that it is about content. It is more about some important factors of contemporary businesses and especially startups:

- Scale Economies - the more you invest the more profit you get

- Network Economies - platform business models like Amazon

- Counter Positioning - Vanguard and ETFs

- Switching Costs - this is why Facebook still exists

- Branding -

- Cornered Resource - patents etc.

- Process Power - this is why governments still exist :)

Assume we have a series of N value 4, 7, 3, ..., 9, 2, 5. We use them for training a model. Now we forget about this data (for whatever reason). We receive a completely new series like 2, 3, 7, 5, 4, 5. Our goal is to forecast the next value of this new series. How can we find it using statsmodels?

One problem I have with statsmodels is that I cannot apply trained models to new data rather than to the train data. In other words I do not want to forecast the train data - I want to forecast completely new time series.

For example, here I create and train a model:

    model = ARIMA(df.value, order=(1,1,1))
    fitted = model.fit(disp=0)

And then I immediately do forecast:

    fc, se, conf = fitted.forecast(...)

Yet, it is not what I need. Typically, I store the model and then apply it to many new datasets which are received later.

sklearn explicitly supports this pattern (train data set for training and then another data set for prediction).

Is it possible in ARIMA and other forecasting algorithms in statsmodels?

It seems that the main proposal is to make SQL watermark-aware. This additional "knowledge" will allow for producing groups and windows taking into account some late and out-of-order records.

I like that I can clikc a word in the target box and choose an alternative translation while the system updates the result accordingly. Previously, I had to play with different translation alternatives manually in Google or Yandex translate. I think deepl also learns from such user choices.

There is a new online translator, http://deepl.com, which relies on deep learning techniques and provides higher (semantic) quality and accuracy of translation. Previously I had quite positive experience with http://translate.yandex.com (but I had to manually compare and combine their results with google translate).

Who knows, maybe they were. But they could interpret them differently. A kind of "alternative honesty" and "alternative openness".