Local stationarity provides a natural framework for time series whose dependence structure evolves smoothly over time. In high-dimensional settings, however, its classical definition can break down, as approximation errors accumulate across time-varying coefficients. To address this limitation, we introduce $(ε_n,δ)$-stationarity, a generalization of local stationarity that holds locally in time and allows approximation errors to grow with the dimension, and we combine it with local sparsity, which lets the set of relevant predictors change over time. We show that, for high-dimensional time-varying autoregressions, the rate of stationary approximation is driven by local sparsity, so that local sparsity governs not only estimation but also the stationary approximation itself. We then develop a \textit{local lasso} with time-varying penalties that selects relevant predictors and their periods of activity, and we derive error bounds that depend on the local sparsity, quantify the cost of nonstationarity, and guide penalty and bandwidth selection. For inference, we propose a desparsified local lasso based on local nodewise regressions and establish a linear representation with an explicit smoothing bias. Asymptotic normality then follows under two bandwidth conditions, one for coefficient variation and one for nonstationarity. Simulations and applications to macroeconomic and neuroscience data illustrate the methods.

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