We consider the problem of online linear regression on arbitrary deterministic sequences when the ambient dimension $d$ can be much larger than the number of time rounds $T$. In this framework we prove deterministic online counterparts of the so-called sparsity oracle inequalities introduced in the stochastic setting in the past decade. They indicate that the task consisting in predicting almost as well as an unknown high-dimensional target vector is still statistically feasible if this target vector has only few non-zero coordinates. Our online-learning algorithm SeqSEW is based on exponential weighting and data-driven truncation. In a second part we apply a parameter-independent version of this algorithm to the regression model with random or fixed design. In this setting the sparsity regret bounds proved on arbitrary deterministic sequences yield sparsity oracle inequalities with leading constant $1$ which are of the same flavor as in Dalalyan and Tsybakov (2008; 2010) but are adaptive (up to a logarithmic factor) to the unknown variance of the noise if the latter is Gaussian (weaker bounds are also proved under weaker assumptions). The framework of prediction of individual sequences thus offers a unifying setting to address tuning issues in both the random and the fixed design cases.