2) Multiplicative Model This model is the most used model in the decomposition of time series.
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Time Series Analysis Models and Techniques Box-Jenkins ARIMA models : These univariate models are used to better understand a single time-dependent variable, such as temperature over time, and to predict future data points of variables. These models work on the assumption that the data is stationary.
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A causal model is the most sophisticated kind of forecasting tool. It expresses mathematically the relevant causal relationships, and may include pipeline considerations (i.e., inventories) and market survey information. It may also directly incorporate the results of a time series analysis.
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Top 10 algorithms
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