I have two large time series data. Both is separated by 5minutes intervals timestamp. The length of each time series is 3month from(August 1 2014 to October 2014). I’m using R (3.1.1) for forecasting the data. I’d like to know the value of the “frequency” argument in the ts() function in R, for each data set. Since most of the examples and cases I’ve seen so far are for months or days at the most, it is quite confusing for me when dealing with equally separated 5 minutes.
I would think that it would be either of these:
myts1 <- ts(series, frequency = (60*60*24)/5)
myts2 <- ts(series, deltat = 5/(60*60*24))
In the first, the frequency argument gives the number of times sampled per time unit. If time unit is the day, there are 606024 seconds per day and you're sampling every 5 of them, so you would be sampling 17280 times per day. Alternatively, the second option is what fraction of a day separates each sample. Here, we would say that every 5.787037e-05 of a day, a sample is drawn. If the time unit is something different (e.g., the hour), then obviously these would change
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I am working with time-series data in Python to see if variables like the time of day and the day of the month and the month of the year affect attendance at a gym. I have read up on encoding the time series data cyclicly using sine and cosine. I was wondering if you can do the same thing for the day of the month. The reason I ask is that, unlike the number of months in a year or the number of days in a week, the number of days in a month is variable (for example, February has 28, whereas March has 31). Is there any way to deal with that?
Here is a link describing what I mean by cyclic encoding: https://ianlondon.github.io/blog/encoding-cyclical-features-24hour-time/
Essentially, what this is saying is that you can't just convert the hour into a series of values like 1, 2, 3, ..., 24 when you are doing machine learning because that implies that the 24th hour is further away (from a euclidean geometric perspective) from the 1st hour than the 1st hour is from the 2nd hour, which is not true. Cyclical encoding (assigning sine and cosine values to each hour) allows you to represent the fact that the 24th hour and the 2nd hour are equidistant from the 1st hour.
My question is that I do not know if this cyclical conversion will work for days in a month, seeing as different months can have different numbers of days.
You can implement this by dividing each month into 2π radians; then in a 28-day month, a day is 0.2234 while in a 31-day month, a day is 0.2026.
This obviously introduces a skew where a shorter month will appear to take up as much time as a longer one; but it will satisfy your requirement. If you only use this metric for normalizing a single feature, that should be inconsequential, and let you achieve the stated goal.
If you have points in time with a finer granularity than a day, you obviously can and probably should normalize those into the same projection.
I am using LSTM for forecasting the stock prediction , I did some feature engineering on time series data .I have two columns , first is price and second is date . Now I want to train the model which take the value of price after every ten minutes . Suppose :
date_index date new_price
08:28:31 08:28:31 8222000.0
08:28:35 08:28:35 8222000.0
08:28:44 08:28:44 8200000.0
08:28:50 08:28:50 8210000.0
08:28:56 08:28:56 8060000.0
08:29:00 08:29:00 8110000.0
08:29:00 08:29:00 8110000.0
08:29:05 08:29:05 8010000.0
08:29:24 08:29:24 8222000.0
08:29:28 08:29:28 8210000.0
Lets say the date comes first is 8:28:31 it will takes the value of its corresponding price and at the second time it should take the value of corresponding columns after ten minutes means 8:38:31 , and sometimes this time does not available in data . How to do it . My goal is just to train the model on after every 10 minutes or 15 minutes ?
The main keyword you are looking for here is resampling
You have time-series data with unevenly spaced time stamps and want to convert the index to something more regular (i.e. 10 minute intervals).
The question you have to answer is: If the exact 10 minute timestamp is not available in your data, what do you want to do? Take the most recent event instead?
(Let's say the data for 8:38:31 is not available, but there's data for 8::37:25. Do you just want to take that?)
If so, I think something like df.resample(_some_argument_to_set_interval_to_10_minutes).last() should work, where I forgot the exact syntax for setting the interval in the resample method. Might be something like 10m or something.
See here:
https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling
I have a huge dataset spanning 2 years (almost 4M rows) which includes every day (each day has different values for the same variable; it has the exact same date with no difference in timing).
Can this be modelled in time series models (ARIMA/SAMIRA)? I only found stuff about multivariate time series forecasting datasets but unfortunately, this is not my case.
Each date has a different number of rows. I'm also not sure how much periods I have to assign.
I am working on a data set that has epoch time. I want to create a new column which splits the time into 10 mins time interval.
Suppose
timestamp timeblocks
5:00 1
5:02 1
5:11 2
How can i achieve this using python.
I tried resampling but i cannot able further process.
I agree with the comments, you need to provide more. After guessing what you're looking for, you may want histograms where intervals are known as bins. You wrote "10 mins time interval" but your example doesn't show 10 mins.
Python's Numpy and matplotlib have histograms for epochs.
Here's an SO answer on histogram for epoch time.
I'm guessing here, but I believe you are trying to 'bin' your time data into 10 min intervals.
Epoch or Unix time is represented as time in seconds (or more commonly nowadays, milliseconds).
First thing you'll need to do is convert each of your epoch time to minutes.
Assuming you have a DataFrame and your epoch are in seconds:
df['min] = df['epoch'] // 60
Once that is done, you can bin your data using pd.cut:
df['bins'] = pd.cut(df['min'], bins=pd.interval_range(start=df['min'].min()-1, end=df['min'].max(), freq=10))
Notice that -1 on start is to shift the first bin to beginning of each 10 min interval.
You'll have your 'bins', you can rename them to your liking and you can groupby them.
The solution may not be perfect, but it will possibly get you on the right track.
Good luck!
I'm trying to find the maximum rainfall value for each season (DJF, MAM, JJA, SON) over a 10 year period. I am using netcdf data and xarray to try and do this. The data consists of rainfall (recorded every 3 hours), lat, and lon data. Right now I have the following code:
ds.groupby('time.season).max('time')
However, when I do it this way the output has a shape of (4,145,192) indicating that it's taking the maximum value for each season over the entire period. I would like the maximum for each individual season every year. In other words, output should have something with a shape like (40,145,192) (4 values for each year x 10 years)
I've looked into trying to do this with DataSet.resample as well using time=3M as the frequency, but then it doesn't split the months up correctly. If I have to I can alter the dataset, so it starts in the correct place, but I was hoping there would be an easier way considering there's already a function to group it correctly.
Thanks and let me know if you need anymore details!
Resample is going to be the easiest tool for this job. You are close with the time frequency but you probably want to use the quarterly frequency with an offset:
ds.resample(time='QS-Mar').max('time')
These offsets can be further configured as described in the Pandas documentation: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases