Time series analysis is a statistical methodology appropriate for longitudinal research designs that involve single subjects that are measured repeatedly at regular intervals over a large number of observations.
In the world of sensors, the importance of the timestamped data—the time series—, is increasing day by day. There are different kind of analysis techniques to handle and examine its internal structure or the relationships between them. However, the complexity and the depth of the ...
While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called “time series analysis;” which focuses on ...
However, there is a paucity of systematic literature reviews of methods of time series analysis using exogenous variables in the modelling structure, regardless of the application area or methodology. Given the relevance of time series analysis with exogenous variables, the main objective is to fill ...
Research limitations/implications - The paper is based mainly on the case study of a single country and therefore, imposes limitations on the generalizability of some of the findings to the region. As such, availability of a longer time series would have been better. Practical implications - ...
CNN originates from image processing and is not commonly known as a forecasting technique in time-series analysis which depends on the quality of input data. One of the methods to improve the quality is by smoothing the data. This study introduces a novel hybrid exponential smoothing using CNN...
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News Best Part-time MBA Programs rankings can aid their research. A school’s rank should be one consideration – not the lone determinant – in where a student applies. The rankings reflect the peer assessments of academic quality, but other considerations involving location, environment, strength...
One of the most important aspects of time series is their degree of stochasticity vs. chaoticity. Since the discovery of chaotic maps, many algorithms have been proposed to discriminate between these two alternatives and assess their prevalence in real-w
The ARIMA algorithm was added to the Microsoft Time Series algorithm in SQL Server 2008 to improve long-term prediction. It is an implementation of the process for computing autoregressive integrated moving averages that was described by Box and Jenkins. The ARIMA methodology makes it possible to ...