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In this study, a deep denoising recurrent temporal restricted Boltzmann machine network is proposed for long-term prediction of time series. The network is a deep dynamic network model which is stacked by multiple denoising recurrent temporal restricted Boltzmann machines with strong modeling ability for complex high noise time series data. To better deal with high noise data, a random noise is added...
Stock market or equity market have a profound impact in today's economy. A rise or fall in the share price has an important role in determining the investor's gain. The existing forecasting methods make use of both linear (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they focus on predicting the stock index movement or price forecasting for a single company using the...
There are in-numerous applications that deal with real scenarios where data are captured over time making them potential candidates for time series analysis. Time series contain temporal dependencies that divide different points in time into different classes. This paper aims at reviewing marriage of a concept i.e. time series modeling with an approach i.e. Machine learning in tackling real life problems...
Today's dynamic computing deployment for commercial and scientific applications is propelling us to an era where minor inefficiencies can snowball into significant performance and operational bottlenecks. Data center operations is increasingly relying on sensors based control systems for key decision insights. The increased sampling frequencies, cheaper storage costs and prolific deployment of sensors...
Time-series classification is an active research topic in machine learning, as it finds applications in numerous domains. The k-NN classifier, based on the discrete time warping (DTW) distance, had been shown to be competitive to many state-of-the art time-series classification methods. Nevertheless, due to the complexity of time-series data sets, our investigation demonstrates that a single, global...
Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Space-time autoregressive time series modeling is a promising inductive method that uses a small number of parameters and can be used for online monitoring and prediction. In this paper, we develop space-time autoregressive models for urban traffic flow network scenarios...
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