When is a Wave a Wave? Long Waves as Empirical and Theoretical Constructs from a Complex Systems Perspective

While long waves have been seriously discussed by economists for almost one hundred years, to date there is no scientific consensus that particular frequency components are in any way privileged in the undoubtedly fluctuating history of modern economic and political development. This is disappointing for two reasons. First, the demonstration that robust, well-defined periodic components existed would present us with a plausible tool for forecasting. And second, they could (and their purported existence has variously been thought to) provide insight into underlying causal mechanisms that generate the observed patterns. The data, I argue, only provide support for a continuous spectral pattern of a power law, 1/fa. This is borne out in the paper by the analysis of political indicators such as the newly revised Modelski/Thompson sea power index and the Levy great powers conflict data. Claims for underlying low-dimensional chaos are only partly substantiated. Individual peaks at various frequencies in the spectrum are probably only due to “random noise” factors unique to segments of the record and not robust across countries and historical episodes. While one could then play the game of finding ad hoc explanations for why the ‘K-wave’ did not take its expected form in this or that century, from the perspective of the theory of complex dynamics it seems more plausible to conclude that a periodic model is not appropriate. Rather, the underlying model is more likely to be of the self-organized criticality or percolation type, characterized by power-law or fractal behavior rather than well-defined periodicity. I highlight some features common to several models of innovation/ economic dynamics and war/hegemonic cycles, such as highly clustered but nonperiodic critical events and resulting long life cycles of rise and decline, that may serve as a plausible explanatory mechanism for this ‘revisionist’ interpretation of the empirical record on long waves.Economics ;

Forecasting bus passenger flows by using a clustering-based support vector regression approach

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio

Forecasting with time series imaging

Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on the manual choice of an appropriate set of features. Exploiting machine learning methods to extract features from time series automatically becomes crucial in state-of-the-art time series analysis. In this paper, we introduce an automated approach to extract time series features based on time series imaging. We first transform time series into recurrence plots, from which local features can be extracted using computer vision algorithms. The extracted features are used for forecast model averaging. Our experiments show that forecasting based on automatically extracted features, with less human intervention and a more comprehensive view of the raw time series data, yields highly comparable performances with the best methods in the largest forecasting competition dataset (M4) and outperforms the top methods in the Tourism forecasting competition dataset