Uncertainty Quantification in Time Series Analysis The stock market value of the data point connected by the red line is predicted by linear regression using market values as well as Twitter sentiment data and more in a certain period of time (red area).
Ideally, this should result in some form of uncertainty quantification, i.e.: the further the model tries to predict from the known state of the market, the more uncertain the predicted value becomes.
Currently my model predicts the same change of market value no matter how far ahead we are trying to predict.
Here is an visualization of two data points: features from the red zone with the market value difference as label.

What is a good way of finding some kind of uncertainty quantification depending on how far ahead we predict?
Adding a feature "days_ahead" to give some sort of prediction window seems too simplistic, because I do not believe that my model will understand the meaning of it. How can I incorporate this information?
 A: No matter how you slice it, this is effectively a different question - instead of looking for a value Y(X), you're asking about the parameters of a distribution with a mean of Y(X) and variance/standard deviation E(X, deltaT).
I see two broad approaches here:
1) The simpler implementation is to run your current model on some historical data, tweak the lag, then measure the error vs. lag value. If there's a nice trend, you can throw a separate regression on top to estimate the rate of increase in stddev given a delay. 
You could then plug the lag you want to run your main model with into the new regression to get your uncertainty window, and use the main model to predict the mean. 
The nice thing here is that you have zero impact on the performance of the value predictor, but you'd need to babysit the extra model to ensure its predictions make sense.
2) The more fanciful approach would be to go full Bayes, and have a single model predict both a value and its own uncertainty. You would need the delay as  a feature of the model in that case.
This may be more complicated to explain, and may be less performant than a simple LinReg. Note that it may be actually easier to implement than (1), because you might find some out-of-the-box solution, while in (1) you need to create a whole extra model for the error and coordinate the two predictors yourself.
A: This is a classical time series estimation / prediction problem. For a broad overview, see, e.g., Shumway/Stoffer, Time Series Analysis and Its Applications.
One way to solve the problem would be using a state-space approach and, more specifically, a Kalman filter (cf. chapter 6 in the book linked to above). You would identify a state-space model of the form
$$
x(k) = A x(k-1) + v(k)\\
y(k) = C x(k) + w(k)
$$
with internal states $x(k)\in\mathbb{R}^m$, measurements (=daily stock market value) $y(k)\in\mathbb{R}$, process noise $v(k) \in \mathbb{R}^m\sim\mathcal{N}(0, \Sigma_v)$ and measurement noise $w(k)\in\mathbb{R}\sim\mathcal{N}(0, \sigma_w)$. The matrices $A\in\mathbb{R}^{m\times m}$ and $C\in\mathbb{R}^{1 \times m}$, as well as the noise covariances can either be selected manually or (partially) learned using, e.g., maximum likelihood optimization.
To predict the future, you would first estimate the values of the state variables $x(k)$ at time $k=now$ from past data using a Kalman filter. Then, you can simply forward-simulate the first of the two equations above from time step to time step. This will automatically result in continually increasing uncertainty due to the addition of the process noise $v(k)$ in each time step.
If you want to stick with your linear trend model, you can simply choose
$$
A = \begin{bmatrix} 1 & 1 \\ 0 & 1 \end{bmatrix} \quad \text{and} \quad C = \begin{bmatrix} 1 & 0 \end{bmatrix}
$$
and identify the noise covariances from past data. This will result in a linear prediction for the future, with the slope and bias of the straight being identified from the data up to this point.
