Trend-Cycle Decomposition: Implications from an Exact Structural Identification

Trend/Cycle Decomposition of Regime-Switching Processes

We present a new approach to trend/cycle decomposition of time series that follow regime-switching processes. The proposed approach, which we label the “regime-dependent steady-state” (RDSS) decomposition, is motivated as the appropriate generalization of the Beveridge-Nelson (1981) decomposition to the setting where the reduced-form dynamics of a given series can be captured by a regime-switch...

A Steady-State Approach to Trend/Cycle Decomposition

In this paper, we present a new approach to trend/cycle decomposition. The trend of an integrated time series is estimated using the conditional expectation of the steady-state level of the series. Given a nonlinear forecasting model, this steady-state approach can differ in important ways from the related long-horizon forecast decomposition proposed by Beveridge and Nelson (1981). We use gener...

Structural Decomposition Trees: Semantic and Practical Implications

The visualization of high-dimensional data is a challenging research topic. Existing approaches can usually be assigned to either relation or value visualizations. Merging approaches from both classes into a single integrated strategy, Structural Decomposition Trees (SDTs) represent a completely novel visualization approach for high-dimensional data. Although this method is new and promising, s...

Trend mining in social networks: from trend identification to visualization

A four stage social network trend mining framework, the IGCV (Identification, Grouping, Clustering and Visualisation) framework, is described. The framework extracts trends from social network data and then applies a sequence of techniques (“tools”) to this data to facilitate interpretation of the identified trends. Of particular note is the visualisation of trend migrations (changes) that feat...

Trend-Cycle Decompositions