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🚧 Time Series as Dynamical Systems: Neural ODE Basics (#31)
* update checklist * add ts dataset: ECB exchange rate * update some of the dl sections * add neural ode intro * fix bibtex * add example of differential equations * add example of differential equations * add examples of first order ode * planning * update neural ode and dynamical systems * remove empty line * code samples for dataset * code samples for dataset * add neural ode results * mypy only for a module * mypy only for a module * mypy only for a module * test pandoc * test pandoc * add pandoc
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<link href="http://www.nature.com/tpj/TPJ_gta.pdf" rel="documentation"/> | ||
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<name>Sebastian Karcher</name> | ||
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# Dynamical Systems | ||
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A lot of time series data are generated by dynamical systems. One of the most cited examples is the coordinates $x(t)$, $y(t)$, $z(t)$ as functions of time $t$ in a Lorenz system. | ||
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!!! note "Lorenz System" | ||
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A Lorenz system is defined by the Lorenz equations[@enwiki:1186188179] | ||
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$$ | ||
\begin{align} | ||
\frac{\mathrm{d}x}{\mathrm{d}t} &= \sigma (y - x), \\ | ||
\frac{\mathrm{d}y}{\mathrm{d}t} &= x (\rho - z) - y, \\ | ||
\frac{\mathrm{d}z}{\mathrm{d}t} &= x y - \beta z, | ||
\end{align} | ||
$$ | ||
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where $x$, $y$, and $z$ are the coordinates of a particle. | ||
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It is a chaotic system that is very sensitive to the initial conditions. | ||
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!!! note "Dynamical Systems" | ||
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Many real-world systems are dynamical systems. Differential equation is a handy tool to model a dynamical system. For example, the action potentials of a squid giant axon can be modeled by the famous [Hodgkin-Huxley model](https://en.wikipedia.org/wiki/Hodgkin%E2%80%93Huxley_model). | ||
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A naive philosophy to model time series is to come up with a set of differential equations to model the time series. However, finding clean and interpretable differential equations is not easy. It has been the top game in physics for centuries. | ||
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In the following sections, we will discuss a few solutions to model data as dynamical systems. |
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