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.
Related
There are two pandas tables, each containing two columns. In the first, it's time and heart rate. The second is the time and systolic pressure.
I need to write code that creates a third table, which for each pressure measurement in the same row contains the time and value of the nearest heart rate measurement, if it was made necessarily before the pressure measurement and not earlier than 15 minutes.
Third DF must be something like this:
I have tried join/megre/concat with different conditions but don't get what i need.
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
I have searched for a while, but nothing related to my question is found.
So I post a new thread.
I have a simple dataset which is read in by pandas as dataframe, with some daily data starting on 1951-08-01, ending on 2018-10-01.
Now I want to down-sample the data to decadal mean, so I can simply do df.resample('10A').mean()['some data'].
This gives me 8 data points, which are at 1951-12, 1961-12, 1971-12, 1981-12, 1991-12, 2001-12, 2011-12, 2021-12. This indicates that the decadal mean values are calculated for year 1951 separately, years 1952-1961, 1962-1971, etc.
I wonder if it is possible to calculate the decadal mean values every 'structured' 10 years?
for example, the decadal mean values are calculated betwen 1950-1959, 1960-1969, 1970-1979, etc.
Any help is appreciated!
You can calculate the decade separately and group on that:
decade = df['Date'].dt.year.floordiv(10).mul(10)
df.groupby(decade)['Value'].mean()
I have downloaded ten open datasets of air pollution in 2010-2019 (which has been transferred to Pandas DataFrame by 'read_csv') that have some missing values.
The rows are ordered by each day including several items (like PM2.5, SO2,...). Most of the data include 17 or 18 items. There are 27 columns which separately are Year, Station, Item, 00, 01, ..., 23.
In this case, I already used
df.fillna(np.nan).apply(lambda x: pd.to_numeric(x,errors='coerce')
and df.interpolate(axis=1,inplace=True)
But now if the data have missing values from '00' to anytime following, the interpolate function would not works. If I want to fill all these blanks, I need to merge the last day data which is not null and use interpolate again.
However, different days have different items numbers, which means there are still some rows that can't be filled.
In a nutshell, now I'm trying to contact all data by the key of items and use interpolate.
By the way, after data cleaning, I would like to apply to xgboost and linear regression to predict PM2.5. Is there any way recommended to deal with the data?
(Or any demo code online?)
For example, the data would be like:
one of the datasets
I used df.groupby('date').size() and got
size of different days
Or in other words, how to split different days and concat together?
Groupby(['date','items'])? and then how to merge?
Or, is that possible to interpolate from the last value of the last row?
I'm looking at ER data and want to build a time series for the number of patients who arrive at the ER per hour. My data set has patient arrival dates and times in one column (so Row 1 might have '1/12/13, 19:21:12', but converted to a pandas date range.
The data set itself is stored in a pandas DataFrame. The column of dates in the DataFrame is stored as a Series.
How would I go about aggregating and storing the number of patient arrivals per hour and plotting them in a time series? I'd like each data point to be something like "5 patients between 1PM and 2PM on January 15th".
Should be as simple as:
patients.set_index('arrival_time').resample('H', how='count').plot()