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Causal layered analysis (CLA) is a theory and method that seeks to integrate empiricist, interpretive, critical, and action learning modes of research. In this method, forecasts, the meanings individuals give to these forecasts, the critical assumptions used, the narratives these are based on, and the actions and interventions that result are ...
Trend analysis is the widespread practice of collecting information and attempting to spot a pattern. In some fields of study, the term has more formally defined meanings. [1] [2] [3]
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. [1] Typically it involves establishing four elements: correlation, sequence in time (that is, causes must occur before their proposed effect), a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the ...
This method, developed by Sohail Inayatullah, is one of the newest developments in Futurology. Causal layered analysis focuses on "opening up" the present and past to create alternative futures rather than on developing a picture of a particular future. It is concerned with the vertical dimension of futures studies, with the layers of analysis.
Forecasting is the process of making predictions based on past and present data. Later these can be compared with what actually happens. For example, a company might estimate their revenue in the next year, then compare it against the actual results creating a variance actual analysis.
Bayesian structural time series (BSTS) model is a statistical technique used for feature selection, time series forecasting, nowcasting, inferring causal impact and other applications. The model is designed to work with time series data.
Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. [1] [2] Exploratory causal analysis (ECA), also known as data causality or causal discovery [3] is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions.
Demand forecasting, also known as demand planning and sales forecasting (DP&SF), [1] involves the prediction of the quantity of goods and services that will be demanded by consumers or business customers at a future point in time. [2] More specifically, the methods of demand forecasting entail using predictive analytics to estimate customer ...