Questions tagged [time-series]
The time-series tag has no usage guidance.
2
votes
1answer
60 views
Simulate time series given temporal auto-correlation functions
Given a random process $x[n] \in \mathbb{R}$ (say of length $N$) and all correlation functions such as:
\begin{align}
\langle x[i]\rangle\\
\langle x[i]x[j]\rangle\\
\langle x[i]x[j]x[k]\rangle\\
\...
0
votes
1answer
50 views
Detrend data with no clear secular trend prior to Fourier analysis?
I am completing Fourier analysis on many different time series of sediment particle flux exiting an experimental flume. Data is collected at a resolution of 1 Hz for durations ranging from ~5,000 to ...
2
votes
1answer
69 views
Comparing multiple signals for similarity
I have multiple (between 2 and 100) signals and need to determine when a significant number diverge from the rest. We're exploring machine learning techniques, but we also want tackle this as a signal ...
0
votes
0answers
27 views
About the power spectrum and confidence upper limit of a time-series data
For now, I have a coupled system with 5 variables and use the Runge-Kutta method to integrate.
...
2
votes
1answer
31 views
How to make a Power Spectral Density Plot in R
I have a time series point process representing neuron spikes. I have computed and plotted autocovariance using acf but now I need to plot the Power Spectral ...
-1
votes
1answer
69 views
How to find correlation/cross-correlation of two signals in real time?
Considering there are two signals and the signals are real time in nature. Visibly when one signal does up the other signal does down and vice versa. Sometimes both the signals moved towards the same ...
0
votes
1answer
60 views
How good or bad my downsampled data is?
Suppose I have time series data at a one-minute resolution. Now I downsample data by taking mean of every 10-minute window, i.e., after downsampling, 60 readings will reduce to 6 readings. How should ...
1
vote
0answers
49 views
How to estimate noise by eigendecomposition of the variance covariance matrix?
I'm new here so I will try to be as clear as possible.
I am trying to apply some techniques from signal processing framework to denoise financial time series.
I would like to know if what I am ...
0
votes
2answers
76 views
Identify random repetitive patterns
Forgive me if it’s too basic, I finish engineering a while ago.
Given any time series, not periodic, I would like to find any repetitive pattern that is distinct (by some given measurement) and is ...
0
votes
1answer
49 views
How to programmatically find a sharp change in slope
First, apologies if I have the wrong group or this question is far too easy for this group. I am (as you'll see) a newbie. Please point me to another, more appropriate group OR tell me how to fix the ...
0
votes
1answer
31 views
Question on Levinson's proposed discrete form of Wiener filter
The whole foundation of Levinson's discrete version of Wiener filter is based on the assumption of stationarity of a time series, and aims to predict a value based on the past observed values. Now, if ...
2
votes
1answer
102 views
How to reduce latency under mean filter for high noise?
We have a position sensor that under some conditions receives some high frequency noise. We can eliminate that very well with a simple mean filtering.
Unfortunately this causes too much lag when ...
0
votes
1answer
43 views
Why doesn't law of large numbers apply to this stationary time-series?
There's a paragraph in Wikipedia that states the following:
Let Y be any scalar random variable, and define a time-series $\{X_t\}$, by
$$X_{t}=Y\qquad {\text{ for all }}t$$
Then $\{X_t\}$ is a ...
2
votes
1answer
730 views
wavelet decomposition for time series signal
Is it possible to use stationary wavelet decomposition as a tool to extract wavelet features for a time series? I can see how it works for image cases, but for a time series prediction problem say $...
1
vote
1answer
58 views
Reproducing paper results about a wavelet transformation using python [closed]
I have been reading this PhD theis about wavelets and I am trying to reproduce some of the results but I don't know the specific code to use to generate similar results.
The original time series ...
0
votes
0answers
27 views
Time Series Extrapolation from existing past values
Using the Levinson-Durbin algorithm, I am trying to predict the next value in a time series based on previous observations, but the results do not follow the trend of the time series. How can I ...
0
votes
1answer
65 views
Find Amplitude of each period of time series
I would like to determine the of each period amplitude of a time series (e.g. the amplitude of each day). The time series has a constant period of 1 day and varies only in amplitude. I have tried ...
2
votes
1answer
125 views
Linear Predictive coding vs AR modeling
I'm looking for a suitable explanation of the circumstances in which the LPC error polynomial for a discrete time process x[n] is replaceable with an error polynomial categorized under the AR model? I ...
3
votes
2answers
119 views
Auto-correlation function, an inverse problem
$x[n]$ is a complex function $n=0,1,2,\cdots,L-1 $
we assume $x[n]$ is periodic in its index: $x[n+L]=x[n]$
Its auto-correlation function $C[n]$ is uniquely defined as:
$$
C[n]=\sum_{i=0}^{L-1} x[i+...
0
votes
0answers
220 views
How to do cross correlation in real time?
I want to measure the signal similarities between two time domain signals a and b in real time. I do not know where to start. I think cross correlation might help but not sure how. Please guide me :-)
...
0
votes
0answers
15 views
Classify future changes in series-1 using local past changes in series-2
Goal:
classify changes in green series using black series as predictor
specifically, when value of black series increased/decreased and value of green series has not increased/decreased similar ...
3
votes
3answers
115 views
Extracting common signal without knowledge about noise [closed]
Given two noisy time series thought to contain a common signal,
$$ x_1(t) = s(t) + n_1(t), \quad x_2(t) = s(t) + n_2(t), $$
what is the best way to determine $s(t)$ without assuming any distribution ...
2
votes
3answers
502 views
What is the filter with the less phase shift?
I have to analyze a dynamic signal but there is too much noise so I applied low pass filter but then there is too much phase shift.So what is the most reactive filter I can apply to my signal ?
Best ...
0
votes
0answers
1k views
Applying a wavelet transform to a 1D time series
I'm preprocessing a non-stationary financial time series as input for a machine learning algorithm, and I'm trying to reduce noise in my data. All the research I've looked at points towards wavelet ...
0
votes
3answers
870 views
Window period(overlap) and FFT
How does changing the window period (i.e the number of points overlap between two frames) affect the FFT results ?
Suppose that a time series signal was converted to frequency domain by FFT with ...
4
votes
3answers
723 views
Need a better step detection algorithm
I have a time series with lots of steps/jumps (data file here). A plot is given below. I would like to subtract an appropriate value for each of these square wave features to bring them back down to ...
0
votes
1answer
172 views
Outlier Detection after Detrending a Time Series With Missing Values or NaN
Goal
Substitute outliers in a time series by most recent valid data
Problem
The time series (end-of-day stock prices) has several 'uncomfortable' properties:
It is non-stationary and can have ...
0
votes
0answers
177 views
Average of autocorrelation functions
Let $X$ and $Y$ are two non-random time series of length $N$ with $\rho_i^{xx}$ and $\rho_i^{yy}$ are the autocorrelation functions of lag $i$, respectively. What is the autocorrelation function of $Z$...
1
vote
0answers
130 views
Estimate standard deviation of random-walk using Kalman filter
I'm new to Kalman filters so this might be a stupid question.
I created a Kalman filter that takes in time series observations and estimates the mean of that time series. This is simply modeling a ...
3
votes
0answers
50 views
Minimum time delay that can be estimated between two sensors
Consider a 1-dimensional toy problem. I have two sensors at different points along the $x$-axis. Somewhere away from the sensors, a disturbance is created which travels towards the sensors, first ...
0
votes
2answers
332 views
How to Mesure the smoothness of a signal
so how can i determine if a signal is smooth or not ? And if its possible to get something indicating the level of the smoothness of my signal.
I looked at the r-squared and hurst exponent but i ...
2
votes
1answer
93 views
First Differentatior and Integrator
For discrete time series $Y$, first differentiation ($D_i=Y_i-Y_{i+1}$) and integrator ($S_i=Y_i+Y_{i+1}$) can be defined as two highpass and lowpass LTI digital filters. Where, transfer function for ...
1
vote
1answer
204 views
Pattern recognition in time series 4x3000 vector
I have a vector, here is a sample of some data from some heat flow data:
I would like to identify features in this image. In the example above I have identified one feature I would like to ...
0
votes
1answer
654 views
Show That the Power Spectrum Density Matrix Is Positive Semi Definite (PSD) Matrix
Given a Wide Sense Stationary Multi Variate (Vector) Random Process $ \boldsymbol{x} \left[ n \right] $ it Auto Covariance Matrix Function is given by:
$$ {R}_{x, x} \left[ m \right] = \mathbb{E} \...
1
vote
1answer
93 views
Comparing two time domain signals in a scale and shift-invariant way
I have two signals in the time domain, call them $S_1(t)$ and $S_2$(t). Because of calibration issues relating to the underlying devices that these signals are obtained from, they will not necessarily ...
0
votes
1answer
53 views
data analysis query
The hydrophone data is in analog-to-digital converter units. The 64000 sps data is 24 bits and the 375 ksps data is 16 bit.
The volts per bit conversion factor for the 24-bit channels is 1....
1
vote
1answer
66 views
Normalization of signal against reference
I am trying to figure out if there is any standard way of normalizing a sequence such as a time series against a reference sequence. I am not an expert on signal processing but I was hoping someone ...
1
vote
0answers
57 views
Auto-correlation of time signals [closed]
I'm interested in papers which are about auto-correlations of periodic time series signals.All relevant papers and applications are interesting to me, as I am studying the properties of the auto-...
0
votes
0answers
59 views
Time delay estimation using two sets of signals?
I am quite a newbie in signal processing and I run into some problem that I am not sure how to solve it. So here goes,
I have two sets of noisy signals, let's call it $x(t)$ and $y(t)$. And another ...
1
vote
0answers
76 views
Why prediction by AR model causes time lag
Why does prediction by AR model cause a time lag?
Please tell me why theoretically.
1
vote
1answer
44 views
Index of stationarity of a time domain signal
During my internship, I've come across a software that calculates a percentage called "Index of stationarity". This index shows how much stationary is the signal. Does anyone have any idea on how to ...
0
votes
0answers
116 views
Filter time series with variable cutoff
I am looking for a practical way to filter a time series with a cutoff frequency which will be increased during the period of the data.
This is not a real time application. The data will be in an ...
1
vote
2answers
83 views
What is the type of these signals?
I want to do some DSP and Machine learning experiments on Electrical and Acoustic signals, but, due to some language setbacks, I didn't know how to call the type of my signal, what I use to google now,...
0
votes
0answers
652 views
How to make a long time-spectrogram for seismic data
I am trying to make a long time spectrogram for seismic data using obspy to understand the spectral content of my dataset.
A simple example given in their documentation is the following:
...
1
vote
1answer
111 views
Issue with the time vector returned by $\tt signal.spectrogram$ function
I'm puzzled by some very simple concept with building up a spectrogram.
Here is a toy example of the issue:
...
0
votes
0answers
61 views
Estimate signal value in real time
Please forgive any stupidity here, I have little experience in signal processing.
If I have a program that polls a device approximately every 5 seconds. How can I estimate the value of the signal ...
2
votes
1answer
125 views
Terminologies - lags, order in time series model
I am facing some difficulties with the terminologies - lag ($p$) and sequence length (number of data points) (N) used in time series model such as Moving average and Autoregressive model. Considering ...
0
votes
1answer
100 views
Time series forecasting on dsp embedded systems
I have a time series analysis algorithm that uses arima modeling and fits a model to the time series and gets the residual values. It is more of a statistical signal processing algorithm than a ...
0
votes
0answers
35 views
Is the time series (ie a realisation) of a stochastic process sufficient to evaluate the autocorrelation matrix of the stationary stochastic process?
I'm evaluating Wiener coefficients for a pulse propagating in an environment that can be modelled as a quasi-stationary stochastic process. I have a collection of 5000 pulses. Each pulse is affected ...
2
votes
1answer
270 views
Predicting account balance based on historical data
Given the values of a bank account balance over time (see figure below as an example), how can one predict the account balance at a given date in the future ?
Should I just fit one linear regression ...
