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Data Statistics and Analytics
Published in Paresh Chra Deka, A Primer on Machine Learning Applications in Civil Engineering, 2019
Time series analysis is a statistical technique that represents time-series data or a trend analysis. This will represent the raw data in a graphical view. This method is used to analyze time-series data in order to extract meaningful information. Time is represented by the X-axis and the data series observations are represented by the Y-axis. ‘Time series’ means that the data is from a series of particular time periods or intervals. Time series, cross sectional, and pooled data are the types of data considered for the analysis. In time-series data, the data is from observations of a variable at different periods of time. Cross-sectional data represents multiple variables collected at the same point in time. Pooled data is a combination of time series and cross-sectional data. Time series forecasting is applied to predict future values based on values observed in the past. An interrupted time series is the analysis of interventions on a single time series. They are different from spatial data analysis where the observations typically relate to geographical locations. A stochastic model in a time series is more accurate when the observations are closer than when they are further apart. A time-series analysis can be applied to real-valued, continuous, and discrete numeric data. There are two solution techniques for time series: frequency domain and time-domain methods. Spectral and wavelet methods are the methods of solution followed in a frequency domain technique, whereas autocorrelation and cross-correlation are applied to the domain method. Figure 7.2 shows time series analysis with random data and a best-fit line.
Impacts of energy-efficiency investments on internal conditions in low-income households
Published in Building Research & Information, 2018
Wouter Poortinga, Shiyu Jiang, Charlotte Grey, Chris Tweed
Interrupted time-series analyses typically include a time variable (indicating the time elapsed since the start of the study, as measured in days) and a time after the interruption variable (indicating the time elapsed since the intervention, as measured in days) in order to identify trends over time and changes in the trend after the intervention, respectively (cf. Lopez Bernal et al., 2016). However, as no obvious trend over time was observed within the baseline and follow-up periods, these terms were excluded from the regression models.
Intervention analysis of the safety effects of a legislation targeting excessive speeding in Canada
Published in International Journal of Injury Control and Safety Promotion, 2018
Suliman A. Gargoum, Karim El-Basyouny
Intervention analysis, which can also be referred to as interrupted time series analysis, involves assessing the effects of an intervention by introducing an intervention term into the ARIMA model. The intervention term is represented through a transfer function, which models the behaviour of the change in the series.