If one has a time series dataset, that contain columns of item number, Date, qty_item_sold. If the frequency of the dataset is 'MS'(Month start) and there are missing value('0.0') in some months due to the lack of purchase orders for those Items how does one handle this type of data set and prepare it for forecasting. Do we drop the rows containing the null values, or do we apply time series missingness mechanisms to fill them in?
I tried dropping the rows and applying statsforecast using models such as AutoArima, AutoETS, Naive. But I don't think the models would are forecasting the dataset properly.
How is the specification of your model. Without knowing what and how you want to forecast it is impossible to give advice. It seems , that you want to find out trends for different goods (maybe with a seasonal component). In this case, one could perhaps change from monthly data to quarterly, semiannual or annual aggregates.
This makes things easier. Obviously, the data are for sales to only one or a few buyers, who order, when their stocks are running down. I recommend to look at a diagram to discover any regularities. For a trend calculation, I would take moving annual averages. By the way, up to now, I can only guess what you really want to analyse.
"Moving annual averages" should mean "12-month moving averages", such that ypu have the average (or the sum) of the past 12 months for every month as a time series.
Handling missing values in time series data can be challenging as missing values can impact the validity and reliability of the results. There are several methods to handle missing values in time series data, including:
The choice of method will depend on the specific characteristics of the time series data, such as the frequency of missing values, the type of data, and the research question. In general, multiple imputation is considered the best method for handling missing values in time series data as it provides more robust results compared to other methods.
It seems, that there is not really a missing value problem, but the problem to analyse time series with many (correct!) zeros: only some months with sales, no sale in other months (i.e. a stochastic process, where not only the values are random, but also the time index). This is not easy to analyse with the usual econometric methods. If one replaces the zeros by "fill-in", one would get a highly wrong picture.
Up to now, I can only guess about the nature of your data and what you want to do with them. It seems that it is sales of a firm (measured in weight, length, volume, amount in ...?) to another firm, which itself sells from its stocks and orders, when its stocks are run down to a critical limit. Without more information, it is hardly possible to help you with the specification of a testable model.
This is the raw data I was practicing with. What is the best way to handle this dataset Anton Rainer.
First you should know what the items are and what dimension the sales are (pieces, tons, barrels,.....?). You should know, to how many buyers the goods are sold. You should also change the format of the data table:
Year Month 5 13864 13867 etc.
2015 1 0 0 0
2015 2 0 53 0
thanks so much Anton Rainer. How do I reach you? Maybe through LinkedIn.
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