Modelling Techniques for Trend Analysis
Identifying a trend is not the same as understanding it. Modelling techniques for trend analysis help analysts move beyond simple visual patterns by applying structured approaches to interpret change over time, compare alternative explanations, and support more reliable, evidence-based decision-making.
Technique Overview
Modelling Techniques for Trend Analysis Definition
Modelling techniques for trend analysis are statistical and computational methods used to identify, represent, and interpret patterns of change within data over time or across variables. These techniques include linear regression, smoothing methods, and non-linear models, each providing different ways to capture underlying structure in data. Trend modelling supports both explanatory and predictive analytics, enabling analysts to understand past behaviour and inform future decisions (James et al., 2021; Shmueli, 2010).
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Business Evidence
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Further Reading
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Modelling Techniques for Trend Analysis references (4 of up to 20) *
- Cleveland, R.B., Cleveland, W.S., McRae, J.E. and Terpenning, I. (1990) ‘STL: A seasonal-trend decomposition procedure based on Loess’, Journal of Official Statistics, 6(1), pp. 3–73.
- James, G., Witten, D., Hastie, T. and Tibshirani, R. (2021) An Introduction to Statistical Learning. 2nd ed. New York: Springer.
- Makridakis, S., Spiliotis, E. and Assimakopoulos, V. (2020) ‘The M4 Competition: 100,000 time series and 61 forecasting methods’, International Journal of Forecasting, 36(1), pp. 54–74. doi:10.1016/j.ijforecast.2019.04.014.
- Perron, P. (1989) ‘The great crash, the oil price shock, and the unit root hypothesis’, Econometrica, 57(6), pp. 1361–1401. doi:10.2307/1913712.
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