Improving time series modeling by decomposing and analyzing stochastic and deterministic influences

• We present an approach to decompose time series into components.
• The obtained components allow understanding stochastic and deterministic influences.
• By knowing such influences we can estimate models with high accuracy.
• A new measure evaluates the behavior of the predicted and expected observations.
• Experiments confirmed improvements by modeling stochastic/deterministic influences.

This paper proposes a new approach to improve time series modeling by considering stochastic and deterministic influences. Assuming such influences are present in observations, a first decomposition step is required to split them into two components: one stochastic and another deterministic. As second step, models are adjusted on each component and combined to form a hybrid model improving time series analysis. The proposed approach considers the Empirical Mode Decomposition method and a Recurrence Plot-based measurement to decompose and assess stochastic and deterministic influences. Experiments confirmed improvements in time series modeling.