A framework for state-based time-series analysis and prediction

Time-series analysis and prediction play an important role in state-based systems that involve dealing with varying situations in terms of states of the world evolving with time. Generally speaking, the world in the discourse persists in a given state until something occurs to it into another state. This paper introduces a framework for prediction and analysis based on time-series of states. It takes a time theory that addresses both points and intervals as primitive time elements as the temporal basis. A state of the world under consideration is defined as a set of time-varying propositions with Boolean truth-values that are dependent on time, including properties, facts, actions, events and processes, etc. A time-series of states is then formalized as a list of states that are temporally ordered one after another. The framework supports explicit expression of both absolute and relative temporal knowledge. A formal schema for expressing general time-series of states to be incomplete in various ways, while the concept of complete time-series of states is also formally defined. As applications of the formalism in time-series analysis and prediction, we present two illustrating examples.

Theories of the state

[Introduction] The recent, unparalleled ascendancy of the liberal democratic state may seem to render alternative theories of the state redundant. But while the prevailing view might be that “I have seen the future, and it works”, it was not so long ago that this was said about a very different type of state. And while the liberal democratic state is an abundant form of government, in practice this often reflects an uneasy compromise of conflicting conceptions of politics. It thus remains important to unpick the theoretical underpinnings of conceptions of the state.