I hope that this question has not be asked like this elsewhere, if so I could not find it during my google research..
I have the following problem: I have data sampled from different sensors("ID") placed in different machines("machine") across the population. The data is coming in very sparse, so I do not have individual time series for every sensor. The value the sensors measure is continuos ("value" in the example data)
My goal is now, with the data accessible atm, to cluster these sensors (in different product lines) and obtain a time series for every type (e.g. types "A","B","C" below). The objective would then be to analyse whether they expose a different trend by sensor type over time.
The data could look like this:
import numpy as np import matplotlib.pyplot as plt import pandas as pd dates = pd.date_range(start='01-01-2015',periods=2000,freq='D') IDs = np.arange(1,177) machines = np.arange(1,21) np.random.seed(2018) ## just creating some random data that resembles the structure of my data rand_dates = np.random.choice(dates,200) ## every machine has sensors of all 4 types, a sensor can move from one machine to another rand_machines = np.array([[np.random.choice(machines)]*4 for i in range(50)]) rand_machines = rand_machines.reshape(200,) rand_IDs = [np.random.choice(IDs[(IDs>=((i-1)%5)*46)&(IDs<=(i%5)*46)]) for j in range(50) for i in range(1,5)] rand_values = np.random.rand(200) df = pd.DataFrame(data=[rand_machines,np.array(rand_IDs),rand_values]).transpose() df.index = rand_dates df.columns = ['ID','value','machine'] df.columns = ['machine','ID','value'] df.index.name = 'date' df['sensor_type'] ='A' df['sensor_type'][df.ID>46]='B' df['sensor_type'][df.ID>92]='C' df['sensor_type'][df.ID>138]='D' df.head() machine ID value sensor_type date 2018-06-28 11.0 7.0 0.209688 A 2016-09-06 11.0 52.0 0.290023 B 2015-08-15 11.0 93.0 0.957608 C 2017-02-16 11.0 154.0 0.576920 D 2017-07-06 1.0 40.0 0.601943 A
One problem (as illustrated by the example-data above) is that the samples are not sampled at any regular frequency, they just come in when they are reported (externally).
I hoped to treat them as time series (i.e. 4 different time series, one for each sensor type) and then try something like DTW with subsamples growing in length. My concern with this was the different sample frequency and the fact that they are basically sampled at different machines, which raised the question whether I can treat this as a time series at all.
Then I came across Linear Mixed Effects (I'm not really familiar with it, so I do not know the whole statistics behind and I do not know if I will have the time to read myself completely into it, as the problem described here is not of the highest importance).
Anyway, I was surprised by the possibility to include almost anything into the linear mixed models, as people seem to be doing, so I gave it a try in Python. Remember, my goal is to identify whether the subtypes expose different trends over time - so i thought I could include the time as an independent variable by arbitralily converting it to seconds:
df = df.reset_index(drop=False) df.date = (df.date - min(df.date))/np.timedelta64(1, 's')
Then I would include this as an indepent variable in the Linear Effects Model and check for the interaction with sensor_type (grouped by machine, as every machine contains all different sensor types). This is how I would specify the model:
import statsmodels.api as sm import statsmodels.formula.api as smf md = smf.mixedlm("value ~ date*sensor_type",data=df,groups = df["machine"]) mdf = md.fit() print(mdf.summary()) Mixed Linear Model Regression Results ================================================================ Model: MixedLM Dependent Variable: value No. Observations: 200 Method: REML No. Groups: 18 Scale: 0.0798 Min. group size: 4 Likelihood: -116.5445 Max. group size: 24 Converged: No Mean group size: 11.1 ---------------------------------------------------------------- Coef. Std.Err. z P>|z| [0.025 0.975] ---------------------------------------------------------------- Intercept 0.525 0.071 7.441 0.000 0.387 0.664 sensor_type[T.B] 0.087 0.114 0.765 0.444 -0.136 0.309 sensor_type[T.C] -0.016 0.109 -0.150 0.881 -0.230 0.198 sensor_type[T.D] -0.048 0.111 -0.437 0.662 -0.265 0.168 date -0.000 0.000 -0.104 0.917 -0.000 0.000 date:sensor_type[T.B] -0.000 0.000 -0.378 0.705 -0.000 0.000 date:sensor_type[T.C] 0.000 0.000 0.987 0.323 -0.000 0.000 date:sensor_type[T.D] 0.000 0.000 0.171 0.864 -0.000 0.000 groups RE 0.000 0.008 ================================================================
This does not converge, but I guess it is because of the randomness of the data (on my data it does converge, the structure of the data is similiar)
Now I Have some questions:
1) Does this make sense what I am doing above, i.e. am I adressing the question I want to adress (different trends over time for different sensor types?)
2) How could I now include additional random effects in this model? I think the sampling-time should also be somehow modelled as having a random effect ( as I have no possibility to control it), but how could I do this?
Also I have the sensor IDs as another group that is nested in the machine, but at various sample points this can change (sometimes sensor gets used in another machine). So can I include this via the "vcf" parameter? (when I do my model does not converge...
3) Is there any requirements to the number of samples per grouping variable (e.g. do I need at least X samples from a machine to make it a meaningful group)
4) Reporting the effects: Can I use the typical p-value heuristics here to report a significant effect? Or how should I determine whether there is a different trend or not?
Thanks a lot in advance to you guys ! (I can use preferably Python or also R for my analysis)
I would be also glad for any comprehensive links how to deal with similiar kind of problems. Thank you guys!