I noticed that the global offset calculation routine used by model derivative service with modern method was changed between June and July 2022.
I transformed the exact same IFC file on these dates. Loading the two urns together on the viewer they do not overlap.
Can you confirm it?
Is it possible to obtain the information of which algorithm was used in the transformation?
Related
I took some measurements with TwinCAT Scope and exported the results as a CSV. The measurement series started at 17 August 2022 at 10:32:25.290 which has timestamp 133051987452906875. This is not UNIX time, because that time would correspond to 1660725145. Adding some miliseconds would add some zeros at the end.
So what is the unit of the TwinCAT timestamp?
The same time format is also used at more places. For example in ADS. From the C++ ADS library I found that the unit is
the number of 100-nanosecond intervals since January 1, 1601 (UTC)
This is the value which I have,
Sun Mar 29 2020 02:55:00 GMT+0530
and I want to get,for example
Asia/Calcutta
as ouput. Thanks in advance.
Offset does not indicate zone
get TimeZone value using time stamp
No.
You cannot determine a time zone from an offset.
Many time zones can share the same offset-from-UTC (the number of hours-minutes-seconds ahead or behind the prime meridian).
See the list of time zone names in Wikipedia. Click on the column header to sort by offset. Notice how often several zones share the same offset.
Specific to your example, notice how we currently have two zones that coincidentally share an offset of five and a half hours ahead of UTC:
Asia/Kolkata (India)
Asia/Colombo (Sri Lanka)
So, without further input, there is no way to know if the author of your input string intended India time or Sri Lanka time.
By the way, the name Asia/Calcutta has been changed to Asia/Kolkata. If your system has no such name, then your tzdata is several years out of date. Always keep all the copies of tzdata up-to-date in OSes, database servers such as Postgres, and runtimes such as Java.
Another complication: politicians frequently change the offset used in their jurisdictions.
So while all of India today uses the same offset of +05:30, that has not always been the case, nor is it likely to always be true in the future (based on the history of how often zones change around the world).
ISO 8601
The ISO 8601 standard defines many sensible formats for representing date-time values as text.
2020-01-23T12:34:56.123456789+05:30
The java.time framework built into Java 8 and later extends one of those format wisely by appending the name of the time zone in square brackets. I suggest using this format if feasible.
2020-01-23T12:34:56.123456789+05:30[Asia/Kolkata]
I have a dataset like below. I have datetime column as index, type is a column with sequence. For ex; R,C,D,D,D,R,R is a sequence.
start_time type
2019-12-14 09:00:00 RCDDDRR
2019-12-14 10:00:00 CCRD
2019-12-14 11:00:00 DDRRCC
2019-12-14 12:00:00 ?
I want to predict what would be the next sequence at time 12:00:00? which is the best algorithm to predict the next sequence?
I know that we can use Markov chain to predict the probable sequence. However, are there any other better algorithms?
Thanks
you can use from knn,svm for prediction.but the first of all you have to change database and define feature for training dataset for example
you can use from another method base on deep learning , I think this link can help you
https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
LSTMs have an edge over conventional feed-forward neural networks and RNN in many ways. This is because of their property of selectively remembering patterns for long durations of time.
LSTMs on the other hand, make small modifications to the information by multiplications and additions. With LSTMs, the information flows through a mechanism known as cell states. This way, LSTMs can selectively remember or forget things. The information at a particular cell state has three different dependencies.
Let’s take the example of predicting stock prices for a particular stock. The stock price of today will depend upon:
The trend that the stock has been following in the previous days, maybe a downtrend or an uptrend.
The price of the stock on the previous day, because many traders compare the stock’s previous day price before buying it.
The factors that can affect the price of the stock for today. This can be a new company policy that is being criticized widely, or a drop in the company’s profit, or maybe an unexpected change in the senior leadership of the company.
These dependencies can be generalized to any problem as:
The previous cell state (i.e., the information that was present in the memory after the previous time step).
The previous hidden state (this is the same as the output of the previous cell).
The input at the current time step (i.e., the new information that is being fed in at that moment).
Maybe this link and method could help you
https://www.bioinf.jku.at/publications/older/2604.pdf
https://www.analyticsvidhya.com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/
My dataset has multiple variables and I am using TSModel for forecasting. I have data till December 2017 but a lot of them are either 0 or missing. During the forecast, it is starting the forecast from July 2015 onwards whereas it should actually start from January 2018.
Can someone help with what might have gone wrong?
If for a particular series all values after a given date are missing, then the estimation period for that series ends at the last observed value's date, and the beginning of the forecast period is the next date.
I am trying to understand Time dimension is SSAS.
In SSAS we have an option to create Time dimension. I have two questions related to it
What difference it makes if i generate, Regular calendar, Fiscal
calendar, Manufacturing Calendar or ISO 8601 Calendar?
Once the dimension is created, is it possible to update it. Let
say i generated it for the range of 1 Jan 2012 to 31 DEC 2012 and
now i want to increase it to 31 Jan 2013 is it possible.
Thanks in advance.
I don't know the answer to #1, but to #2, yes, you will be able to extend the range of your time dimension after it is created.