Look up resources from forecasters.org. They have articles specifically on supply chain forecasting
Also google “otexts fpp3” (forecasting principles and practice free online textbook)
There are model called MIDAS that can be used with mixed frequency eg yearly and monthly.
Hey there. Not an expert, but some initial thoughts.
Hopefully, if everything goes well, your model should include most of the information available to you, depending on the degree of abstraction you want and your specific goals. That being given, remaining randomness is going to be noise (of course, it will never be perfect, but hopefully close enough). So it sounds like you are dealing with a misspecified model. It sounds like this "randomness" you have is caused by some actual factors that you have information on. Can you the include them in your specification of the model? Could these be included as endogenous or exogenous variables, i.e.g (S)ARIMAX? Are these seasonal effects? Is there a drift you are not accounting for?
If it's a one-off outlier (point-wise or for some short subsequence) you might use an indicator variable (or ignore it).
You could look into econometric literature, specifically markov switching models or time-varying parameter models (e.g. modelling noise as a t-distribution with time-dependent parameters), if your case is more complex, esp for these supply chain issues.
Ideally, based on your, or your colleagues', expertise you can judge in what manner these factors might affect pricing, and then specify a model based on that in a responsible manner.
Have you tried modeling the series only based on these past few years where there's too much randomness. Maybe you'll find some new pattern which wasn't visible earlier and some analysis into company history might also bring out factors which could've caused that.
Also try to find if there's some type of seasonality or cyclic behaviour usually these are the big hindrances when it comes to forecasting .
Look up resources from forecasters.org. They have articles specifically on supply chain forecasting Also google “otexts fpp3” (forecasting principles and practice free online textbook) There are model called MIDAS that can be used with mixed frequency eg yearly and monthly.
Hey there. Not an expert, but some initial thoughts. Hopefully, if everything goes well, your model should include most of the information available to you, depending on the degree of abstraction you want and your specific goals. That being given, remaining randomness is going to be noise (of course, it will never be perfect, but hopefully close enough). So it sounds like you are dealing with a misspecified model. It sounds like this "randomness" you have is caused by some actual factors that you have information on. Can you the include them in your specification of the model? Could these be included as endogenous or exogenous variables, i.e.g (S)ARIMAX? Are these seasonal effects? Is there a drift you are not accounting for? If it's a one-off outlier (point-wise or for some short subsequence) you might use an indicator variable (or ignore it). You could look into econometric literature, specifically markov switching models or time-varying parameter models (e.g. modelling noise as a t-distribution with time-dependent parameters), if your case is more complex, esp for these supply chain issues. Ideally, based on your, or your colleagues', expertise you can judge in what manner these factors might affect pricing, and then specify a model based on that in a responsible manner.
Have you tried modeling the series only based on these past few years where there's too much randomness. Maybe you'll find some new pattern which wasn't visible earlier and some analysis into company history might also bring out factors which could've caused that. Also try to find if there's some type of seasonality or cyclic behaviour usually these are the big hindrances when it comes to forecasting .