I have to forecast the future gold loan portfolio growth of a financial firm. I have past 36 month growth. I am planning to use montecarlo simulation to forecast but growth is a deterministic process not a random process. How can I apply montecarlo for this situation?
Please help me friends. Thanks in Advance..
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Hi there is something which is unclear to me when reading the articles and documentation.
They say (https://github.com/facebook/prophet/issues/2112) that the underlying model is continuous.
For me it is hard to understand as normally their X matrix is based on data points at time t no?
For example if I have a weekly seasonality normally i should not use a frequency which is lower or equal to twice a month as the data is sampled weekly.
But under the hood do they interpolate the time series to daily points?
In an experiment with weekly data I have force the weekly seasonality to 8 coefficients and it increases drastically the performances on an unseen validation set ( from 0.3 R2 to 0.6 without weekly and with weekly seasonality which for me doesn't make sense).
I of course predict on weekly data too
So I am not sure whether they first interploate to daily and then perform seasonality computations
Can someone help me understand please?
I have a webpage that keeps track of all the incomes and expenses of a business.
The user can see its businesses total balance any day, from the day the business was created. So for example, if today is 9/15/22 and the business was created on 6/12/21, the user can see the business total balance from any day since 6/12/21.
Calculating the total balance is easy: Incomes - expenses = total balance
The problem is that when the business has a lot of time running, expenses and incomes can be thousands, so querying them all from db and operating with all of them at the same time can be very slow.
Can you think of any other way of keeping track of this? I thought about checkpoints every month, but i dont really know if that is the best idea. Im working with nodejs and mysql. Thanks a lot.
I am currently working on a prototype for a future product. I am currently working on a concept for how highly flexible licences can best be stored and billed.
The following illustration explains the situation a little:
The billing should take place for each month and include the exact prices on a pro rata basis. In the example, January is to be billed, which is made up of 3 different licence periods. Each part should contain the exact price proportionate to its time in the month. So the concept is clear to me, but I am asking about the best technical implementation.
Should the periods be in an SQL database or a timeseries DB or something else entirely? How should I mark when periods have been paid, but extend into the next month?
SQL would be my first approach: Periods table: store periods (startDate, endDate, priceFactor, isBilled)
Problems:
How to bill ongoing periods, which extend over many months? (they have no endDate)
sql-queries could get complex
Thank you for your help!
Instede of reading TMY file in to PvLib, I wants to generate weather data using PvLib function, class or modules.
I have found some of function to generate weather forecast using "from pvlib.forecast import GFS, NAM, NDFD, HRRR, RAP" these modules.
Above mention method/algorithm has some limitation. It generate data for limited period. Some of the modules are generating only for 7 days or 1 months.
Also it gives data for 3 hourly time stamp difference.
Is there any possibility to interpolate weather data for entire year using PvLib?
Forecast is generally meant to be used for future prediction, and is limited in time and accuracy inversely proportionate: longer future forecasts have less accuracy, and accuracy decreases the further in the future it is. For example, the forecast for today is more accurate than forecast for tomorrow, and so on. This is the reason that forecast is limited as you are forecasting for seven future days.
Forecast providers as GFS may or may not provide data for historic forecasts; it depends on the provider and their services.
As I remember, GFS gives prediction in old file fashion, so I moved to providers that gives online REST services forecast, as I become first a programmer and then a data scientist and never a meteorologist.
When timeseries period is not in your required period, you can do some resampling. Extra values will be mathematically calculated with some formula that—as long you don't know the original provider's formula—resample formula will be likely different.
I developing an fitenss app and want to calculate the pace(i.e speed) of a person using the steps he will walk.
For this, I will use certain time interval.
For example, in 1 hour if a person walked 2500 steps and now calculate the pace of a person i.e he is walking slow or moderate or fast.
How pace can be achieved from steps?
Can anyone please help it out?
Thank you