http://msdn.microsoft.com/en-us/library/windows/apps/hh694058.aspx
"Microsoft will pay you an amount equal to the Net Receipts for your
app, minus the Store Fee (the “App Proceeds”) as full compensation for
your app as made available to customers from the Windows Store, on a
monthly basis"
Please specify what the current practical behavior of "on a monthly basis" means. Eg, what is the current implemented payment schedule.
In the instance that Microsoft employees read this, please consider amending the public agreement to specify the expected behavior, eg similar to the Facebook agreement:
Microsoft will make payment of your Developer Balance approximately X number of days following the end of the Timespan period in which the transaction occurred, except as otherwise set forth herein.
Where X is the expected number of days after the particlar Timespan that transactions are grouped into. Eg, X is 2, and Timespan is monthly.
First of all, Microsoft will not pay anything less than a $200(USD) check, so you must clear that hurdle first. Once you trigger over $200 a month steadily, you will get paid regularly on a monthly basis.
Also note that your cut is only 70% of the first $25,000 USD and then 80% on anything after.
As a developer, I do not make the $200 in a month, but it is generally only a day or two after I notice that I have hit the $200 threshold until my bank account gets creditted with the balance.
Related
If someone could please advise on how to achieve the following function on my site: I have a business where I ship live animals to customers. The challenge is communicating the weather forecast with the customer, along with the safest day/s to ship for the animals’ wellbeing. This planning is especially key during the peak summer and winter months when temperatures are very extreme. I am looking to automate these functions during the customers checkout process, giving them control of the shipping date. I am looking to implement something like this on my site.
I found another website where I recently placed an order that provided this relevant information for the customer to review before deciding on what day they would like to have the animal shipment shipped out. The table provided a 5 day forecast, which included temperatures for morning and evening at the point of origin, in transit, as well as the customers’ destination address. Any day/s where the temperature is too hot or too cold was marked as unavailable (red) for shipping. Possible shipping dates consisted of two consecutive days where temps are in the safe range and labeled as available (green) Excluding Friday, Saturday, and Sunday. It is also important for the customer to plan ahead and pick a shipping day based on their availability to be home to receive the package the following morning.
Hello I have a problem with Exchange rates in Dynamics AX 2009.
In TaxTrans table is different exchange rate than in LedgerTrans table.
In PurchParameters table Exchange Date Rate is set to DocumentDate but in TaxTrans is get exchange rate from TransDate.
What's wrong with that? Sounds like normal accounting behavior. You have provided an incomplete question with little to go on, so this answer could be wrong if you add more info.
Let's say your company operates in USD and today is June 10.
If you receive an invoice dated June 1 in CAD, your document date would be June 1, and your exchange rate would be the rate as of June 1. This depends on your accounting, but normally you want to know your cost on the date you accrued it...not the date you happened to transact it. This can give you more accurate costing.
Then when you pay a bill or whatever, the tax will be on the date of the transaction, so you will want the exchange rate on that transaction date.
I'm trying to understand the data required for Amazon Forecast to create a demand forecast using my historical sales data. I've read through the documentation many times. I am still confused as to how the "in_stock" field in a related time series is supposed to function. Here is a link to the description of the "in_stock" field I am referring to:
https://docs.aws.amazon.com/forecast/latest/dg/retail-domain.html#related-time-series-type-retail-domain
It says:
The following fields are optional and might be useful in improving
forecast results:
in_stock (integer; 1=true, 0=false) – A flag that specifies whether the item is in stock.
What exactly is this field meant to indicate? Is it only meant to be set to 0 when the number of sales is 0? In other words, if the number of sales for a given day is 0, and in_stock is set to 0, then the system knows the sales were 0 because the product was not available, not because there was no demand.
What if a product goes out of stock halfway through the day. Would that be a case where you might have in_stock = 0 but also have sales on that day?
I am also confused how this in_stock field comes into play when in another piece of their documentation:
https://d1.awsstatic.com/whitepapers/time-series-forecasting-principles-amazon-forecast.pdf?did=wp_card&trk=wp_card
On page 10, they say:
In the retail case study, the information that a retailer sold zero
units of an available item differs from the information that zero
units of an unavailable item are sold either in the periods outside
its existence, e.g. before its launch or after its deprecation, or in
periods within its existence, e.g. partially out of stock, or when
there was no sales data recorded for this time range. The default zero
filling is applicable in this former case. In the latter, even though
the corresponding target value is typically zero, there is additional
information conveyed in the value being marked as missing. You must
preserve the information that there was missing data and not discard
this information (see the following example for an illustration why
keeping the information is important). To encode a value that does not
represent zero sales of an available product as truly missing, Amazon
Forecast allows the user to specify the filling type for middle fill
and back fill in the FeaturizationMethodParameters key of the
FeaturizationConfig parameter of the create_predictor API. To mark a
value as truly missing, the fill type for these parameters should be
set to NaN. Unlike for zero filling, the values encoded with NaN are
treated as truly missing, and not used in the metrics evaluation
component.
This seems to indicate that when a product is out of stock and there are no sales, those rows should be marked as NaN, which effectively removes those rows from the dataset.
I suppose my questions boils down to:
What is the difference between a day with 0 sales and in_stock = 0 vs a day with sales = NaN, which effectively removes that day from the dataset?
What do you do when a product goes out of stock partway through the day? Can in_stock = 0 and still have sales data for a given day?
The difference is that with in_stock your model includes more information allowing you to do projections based on in_stock into the future. It's better to use in_stock as opposed to sales = NaN as long as you have the historical data.
If you require in_stock to be represented as a partial day, your time series will need to be more granular. If you can't achieve more granularity you should be able to specify sales > 0 and in_stock = 0, all this will do is train the model, for that Id, that when an item is out of stock a certain amount of sales are made hence it is better to have a more granular time series.
For this use case the recommendation would be to specify sales = 'NaN' in the TARGET_TIME_SERIES when a particular product is unavailable / out-of-stock. This is as per the approach outlined in: https://d1.awsstatic.com/whitepapers/time-series-forecasting-principles-amazon-forecast.pdf. The example in Figure 6 (on page 11) elaborates on this further. For cases where a product goes out of stock part way through the day, the sales value should be specified.
It is also worth noting that at the time of this writing, RELATED_TIME_SERIES in Amazon Forecast requires the last timestamp for every item in the related time series dataset must be on or after the last timestamp in the target time series plus the user-designated forecast window (called the forecast horizon). See: https://docs.aws.amazon.com/forecast/latest/dg/related-time-series-datasets.html for additional details.
Hope this helps.
i have created a model in AWS
contains Sales records by date
for example
Type: Sale,Time:2016-08-01,Success:1 (1 is a boolean)
i want to predict how much Sales will be after 1 month from the latest date (2016-08-01)
which means a combo of Type=Sale AND Time >2016-08-01 and Success=1
any idea how to achieve this
thank u
You need to aggregate your data to a wider array of attributes to be able to use Amazon ML for such predictions. You can use different level of aggregation, for example daily, weekly and monthly.
You should also add any relevant information for the items that you are selling. For example, if you are selling umbrellas, you should add information about the amount of rain on that day, or if you are selling flowers, you should add information about day of the week or proximity to holidays, when people are buying more flowers.
So I have made a spreedsheet to help me keep track of my power and gas bill - well the total cost of the bill anyway. This way as the months go on I am able to see what the average Power and Gas bill have been.
I am using only sheet1 and prefer to do it this way, so when I show my partner they don't need to flick through pages of numbers. It's right in front of them with Power & Gas in the next row - take a look at the image below.
So as you can see from the above (not including the J3 row) I have everything laid out in the layout I would like. This way when I get a bill emailed to me I add it, and then I can pay X amount and record that.
I know how to do =sum() and =average() but how can I limit it to only the power rows automatically.
The query should be looking up G column for power or gas and then whichever one it is get the H column next to it and then work out the cost per month by looking at the date the bill came out.
Power bill is every month so that easy, gas is every 3 months.
I know in open office they have the =if() command and I made it return TRUE when it was power but it does not seem that i can use just that function? I am wondering if maybe a TUT or example that outlines how we get the result like this.
Another example would be replace gas and power with tech toys i.e batteries over a year what was the real cost per monthly.
Ok after thinking outside the square - the answer I am using is this.
=AVERAGEIF(G3:G100;"POWER";H3:H100)/COUNTIF(G3:G100;"POWER")
I divided power by it's count as we get it every month
=AVERAGEIF(G3:G100;"GAS";H3:H100)/COUNTIF(G3:G100;"GAS")/3
I divided gas by 3 as we get it every 3rd month