I have time series data of 6 parameters at every minute of 2 months. The data has a lot of peaks and jump as in the line plot below.
I need to predict the values of 2 parameters 5 minutes into the future. Should I interpolate those peaks or leave it as it is? How to clean such time series data?
All of the parameters positively correlated with each other.
Related
I am working on a scientific data analysis project. I need to graph the number of movements on the y-axis and the intervals of time on the x-axis. This dataframe includes over two years worth of daily entries, so when I try to graph all of the data, it is illegible. How can I change the frequency of intervals to be a set amount of time rather than every day?
Date = df['Date']
No_of_Movements = df['Movements']
plt.plot(Date, No_of_Movements)
I have tried graphing the entirety of the data, but the resulting graph was too busy and difficult to read.
I have a high frequency time series (observations separated by 3 seconds), which I'd like to analyse and eventually forecast short-term periods (10/20/30 min ahead) using different models. My hole dataset containing 20K observations. My goal is to come out with conclusions of how good the different models can forecast the data.
I tried first to plot the hole dataset but i couldn't identify anything :
Hole Dataset
Then I plotted only the first 500 observations and this is the result :
Firt 500 observations
I don't know why it looks just like a whitenoise !
After running the ADF test on the hole dataset it gives me a 0.0 p-value ! this means that my dataset is stationary right ?
I decided to try first the ARIMA model, from the ACF and PACF plots I can't identify p and q :
ACF
PACF
1- Is the dataset a whitenoise ? Is it possible to predict in this time series ?
2- I tried to downsample the dataset (the mean in each 4 minutes), but same think, I couldn't identify anythink, and I think this will result a loss of inforlation no ?
3- What is the length of data on which I should fit the ARIMA on the training set ? Does it make sense to use a short training set for short term forecasting period ?
I have the following values for time series which are irregularly spaced and also for some timestamps I got more than one observation. I want to convert this into an equally spaced time series and then fill in the gaps using some form of interpolation. How can I interpolate this time series data in python to make it regularly spaced so that I can apply some ML methods? Can you help me to code this in pythonIn Figure 1st column is my sensor data value and second column is time stamp in seconds. As you can see the time stamp for 21 second is missing and also the sensor value.
In this figure you can see I have multiple observations for 5497 second
I'm comparing two irregulary spaced time series with tslearn implementation of DTW. As both time-series are very irregulary sampled and their sampling isn't correlated, I would like to use Sakoe-Chiba radius to constrain range of compared observation to one hour (for example), if I would have regularly sampled time series in, let say, 1 minute intervals I would use Sakoe-Chiba radius equal to 60, but I don't have such data, it should exists more natural solution then data manipulation (interpolation to 1 minute time interval) for example variable Sakoe-Chiba radius (each observation have different S-Ch radius, precomputed to obtain equivalent of 1 hour constrain), is there reasons that would be computationally inefficient in comparison to constant S-Ch radius ?
The dataset of 921rows x 10166columns is used to prediction bacteria plate count based on water temperature. Each row is an observation(first 10080 columns being the time series of water temperature and the remaining 2 columns being y labels- 1 means high bacteria count, 0 means low bacteria count).
There is fluctuation in the temperature for each activation. For the rest of the time, water temperature would remain constant at 25°C. Since there are too many features in the time series, I am thinking about extracting some relevant features from the time series data, such as the first 3 lowest frequency values or amplitude of the time series using fftor ifftetc fromscipy.fftpack, then fit into a logistics regression model. However, due to limited background knowledge in waves/signal, I am confused about a few things:
1)Does applying fft on the time series produce an array of numbers of the frequencies of the time series data? If not, which function should I use instead?
2)I've done forward fill to my time series data(ie. data points are spaced at fixed time intervals) and the number of data for each time series is the same. If 1) is correct, will the number of frequencies returned for different time series be the same?
Below is a basic visualisation of my original data.
Any help is appreciated. Thank you.