Fair enough. Good luck with your project! Come back if you need help. Regards,
--------- Matt Taylor OS Community Flag-Bearer Numenta On Tue, Oct 27, 2015 at 1:51 PM, Alan Haverty <[email protected]> wrote: > Thank you Matthew, > I'll experiment with the events. > > No, this will actually be a component of my final year project (4th year > college, Ireland) > > I missed the boat for this years challenge, but I'll be sure to join in > next year! > > Thanks again, > Alan Haverty > > On Tue 27 Oct 2015 at 04:23 Matthew Taylor <[email protected]> wrote: > >> Hi Alan, >> >> Here are my comments about your questions. >> >> 1.a. This was an ad-hoc idea, but I haven't tried it. >> >> 1.a.i.-ii. Ideally, you would not want to include this field at all, you >> would just have years worth of data an a learned model that has seen the >> patterns each holiday produced in the past. But since you don't have that >> kind of history, you'll need to experiment a little. Perhaps a simple >> countup isn't going to give you what you want... if a holiday like XMas is >> a big deal, maybe its value is higher and there is a longer countup to that >> date, rather than say St. Patrick's Day. Like I said, this was just an >> ad-hoc idea and I can't say for certain how it will work. You'll want to >> experiment with it. >> >> 2. If you have data for 15 locations, I would say that each location >> should have its own model. One model only make predictions for one field, >> anyway. >> >> 2.a. You would only lose value if there are correlations between the >> locations, but I imagine this is not the case. The frequency of deliveries >> at one restaurant are probably not directly affected by the frequency of >> deliveries at another. >> >> 2.b. No. >> >> By the way, is this an HTM Challenge project? >> >> Regards, >> >> >> --------- >> Matt Taylor >> OS Community Flag-Bearer >> Numenta >> >> On Mon, Oct 26, 2015 at 1:27 PM, Alan Haverty <[email protected]> wrote: >> >>> Hello Nupic, >>> >>> >>> I have some questions about feeding in known events and also, how I >>> should handle multiple 'locations' that have similar properties but that >>> may not be directly related in reality. >>> >>> >>> Please let me know if I'm asking in the wrong mail list. >>> >>> I'm also providing a brief description and example of the project. >>> Outline of Problem >>> >>> Restaurants that offer food delivery are forced to hire drivers, pay for >>> insurance, pay for wages + predict how many drivers are needed in advance >>> and schedule their hours. >>> >>> >>> I propose to abstract this as a service where restaurants can simply use >>> an app to request a driver and let this service-business worry about >>> drivers, insurance, wages, roster scheduling etc. >>> >>> >>> To achieve this, the central ‘delivery system’ needs to predict how many >>> jobs are going to come from each area within a city to allow scheduling of >>> drivers days/weeks in advance. >>> >>> >>> I believe NuPIC is ideal to solve this problem, but I have a few >>> questions that I hope the mailing list can help with. >>> >>> >>> *Assuming for this example:* >>> >>> - That a city is divided into 15 geographical areas. >>> - That I have 3 months of known data with the amount of total >>> deliveries that came from each area per hour. >>> - That I need to predict the number_of_deliveries per hour >>> (days/week in advance, not too concerned with how far in advance yet.) >>> >>> Example Data >>> >>> *Example of 3hrs of data for one of those 15 areas:* >>> >>> *dttm* >>> >>> *number_of_deliveries* >>> >>> *datetime* >>> >>> *int* >>> >>> *T* >>> >>> 2015/08/01 00:00:00.0 >>> >>> 178 >>> >>> 2015/08/01 01:00:00.0 >>> >>> 96 >>> >>> 2015/08/01 02:00:00.0 >>> >>> 52 >>> >>> >>> Questions >>> >>> 1. 1. If I want to incorporate event data for known upcoming >>> events such as a national holiday/football game/TV series finale airing; >>> how should this hourly event data be arranged? >>> >>> a. Matthew Taylor suggested >>> <https://www.youtube.com/watch?v=gYOwBlVuJDw> to use a count down until >>> the hours of the event >>> >>> i. How >>> would this work if I wanted to weight certain events differently? (e.g. A >>> national bank holiday would be weighted higher than a television series >>> episode airing) >>> >>> ii. While >>> the event is occurring, how should the countdown be represented? Should it >>> be ..,5,4,3,2,1,1,1,1,1,…,1,20,19,.. *(Red being the event currently on >>> for that hour(s) or some cases the whole day(s))* >>> >>> 2. 2. I need to do this for multiple locations, would a field to >>> specify each location be correct (Meaning there would be x15 {Saturday @ >>> 12:00}, *one for each of the 15 locations*) or should they be totally >>> separated? >>> >>> a. If I separate locations completely, would you expect I lose >>> value in anyway? >>> >>> b. If I keep them together, could locations contaminate/effect >>> each other that may not happen in reality? >>> >>> *c. **Apologies for this broad question, if anyone could even >>> point me to suggested reading, I would appreciate it.* >>> Thank you for reading! >>> >>> >>> >>> *Best regards, Alan Haverty**[email protected] <[email protected]>* >>> >>> >>
