A multi-scale smoothing kernel for measuring time-series similarity

In this paper a kernel for time-series data is introduced so that it can be used for any data mining task that relies on a similarity or distance metric. The main idea of our kernel is that it should recognize as highly similar time-series that are essentially the same but may be slightly perturbed from each other: for example, if one series is shifted with respect to the other or if it slightly misaligned. Namely, our kernel tries to focus on the shape of the time-series and ignores small perturbations such as misalignments or shifts. First, a recursive formulation of the kernel directly based on its definition is proposed. Then it is shown how to efficiently compute the kernel using an equivalent matrix-based formulation. To validate the proposed kernel three experiments have been carried out. As an initial step, several synthetic datasets have been generated from UCR time-series repository and the KDD challenge of 2007 with the purpose of validating the kernel-derived distance over shifted time-series. Also, the kernel has been applied to the original UCR time-series to analyze its potential in time-series classification in conjunction with Support Vector Machines. Finally, two real-world applications related to ozone concentration in atmosphere and electricity demand have been considered.Peer ReviewedPostprint (author’s final draft

A multi-scale smoothing kernel for measuring time-series similarity

In this paper a kernel for time-series data is introduced so that it can be used for any data mining task that relies on a similarity or distance metric. The main idea of our kernel is that it should recognize as highly similar time-series that are essentially the same but may be slightly perturbed from each other: for example, if one series is shifted with respect to the other or if it slightly misaligned. Namely, our kernel tries to focus on the shape of the time-series and ignores small perturbations such as misalignments or shifts. First, a recursive formulation of the kernel directly based on its definition is proposed. Then it is shown how to efficiently compute the kernel using an equivalent matrix-based formulation. To validate the proposed kernel three experiments have been carried out. As an initial step, several synthetic datasets have been generated from UCR time-series repository and the KDD challenge of 2007 with the purpose of validating the kernel-derived distance over shifted time-series. Also, the kernel has been applied to the original UCR time-series to analyze its potential in time-series classification in conjunction with Support Vector Machines. Finally, two real-world applications related to ozone concentration in atmosphere and electricity demand have been considered.Peer Reviewe

Price dynamics, profit potential, and cannibalisation effect of remanufactured smartphones: Empirical analysis using eBay data

Despite proven benefits of remanufacturing, original equipment manufacturers have yet to fully engage with such an activity due to increased complexities and fears of lost sales. This thesis aims to shed light on the viability of smartphone remanufacturing by empirically investigating live-listing prices of new and remanufactured smartphones on eBay. Using Functional Data Analysis, it uncovers the price dynamics at each life cycle stage, and reveals notable similarities amongst smartphones, regardless of the differences in generations, models, and conditions. This study then explores the relationship between price and volume, and finds that remanufactured smartphones have high profit potential in online secondary markets. By examining the price-volume relationship across multiple product generations, it shows that remanufactured smartphones cannibalise the profit potential of their new counterparts only when the smartphones are mature. These results challenge the belief that remanufactured smartphones are a threat to new smartphones, and signify a future business avenue that is profitable, yet, environmentally-friendly