Hello - If you are considering using time series techniques such as exponential smoothing, then your idea of: "Use Year 1 data to create a model Apply early Year 2 data to predict December of Year 2" will not work. Time series models are closely tied to the data which is used to estimate parameters. This is very different to techniques like OLS regression. For example: you can "train" a predictive model such as a logistic regression on training data, create a score file, and then apply this score file to new data. This concept does not apply to statistical forecasting models - here you should use all history available to estimate the parameters of the model - usually the most recent data is the most relevant. Also, once you have estimated the parameters, these models are usually tied to the history which was used for estimation. For exponential smoothing models for example you can think of floating average with infinite memory but with exponentially falling weights. In my opinion the question of whether to use a predictive model or a statistical forecasting model depends on your business question which you have in mind - note that for both areas very scalable algorithms are available. If you can share a some example data and specify how you would the results to look like, we might be able to come up with some code snippet for you. Thanks, Udo
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