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8

You posted what appears to be incomplete code but if I'm interpreting correctly you fit a model with TimeSeriesModelFit and it returned a model which you then used to create a forecast as such. monthlyObservations = TimeSeries[ WeatherData["KORD", "Temperature", {{2008, 1, 1}, {2014, 12, 31}, "Month"}]]; trendAdded = TimeSeries[ ...


5

For seasonal data you probably want SARIMA which is a more parsimonious way to work with high order ARIMA models. This is especially true given the small amount of data you are working with. You can use TimeSeriesModelFit at various levels of automation. By default it will just try to pick the best model from its list of potential families. mod = ...


1

First Analysis The official (first answer) from the technical support at Wolfram Research states that: This seems consistent with the overall design of Mathematica where the head of an object that is a special case of some larger class generally has the head of the larger class (e.g. plots generally have the head Graphics). [...] this may have some ...


2

Here is an implementation of the regression of the type indicated in the equation. y = {{0, 2.05648}, {1, 2.05648}, {2, 2.05648}, {3, 2.05648}, {4, 2.05648}, {5, 2.05648}, {6, 2.05648}, {7, 2.05648}, {8, 2.05648}, {9, 2.05649}, {10, 2.0565}, {11, 2.05653}, {12, 2.0566}, {13, 2.05681}, {14, 2.05738}, {15, 2.05891}, {16, 2.06304}, {17, ...


1

At least on V9: DateDifference @@@ Partition[BHVTemperatureData[[All, 1]], 2, 1] (* {31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31 ... *) Shows that the data is not "equally spaced"



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