Questions tagged [time-series]

Time series are data observed over time (either in continuous time or at discrete time periods).

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23 views

Is there an eigenfaces equivalent for PCA analysis of time series, eigen-time series?

I am trying to better understand PCA as applied to time series by drawing parallels with this explanation of PCA as applied to images of faces. In particular, I would like to visualize the resulting "...
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final performance veredict given different testing procedures

I have K time series of a certain performance indicator each for a 5-day "reference" period and a 5-day "measurement" period with equal frequencies. The goal is to give a verdict if the performance in ...
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joint probability density function for multinomial [closed]

Let X and Y have the joint probability density function f(x, y) = {k xy , , 0 ≤ x ≤ y ≤ 1 0, otherwise } Find the value of k and compute the correlation coefficient.
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How to combine Two electrode data from a time series dataset of EEG?

I have a dataset consisting of 15 electrode time series data. Each electrode is labeled and there are 5000 data points for 1 sec of EEG Data. I have created a classifier to where I convert 1 sec of ...
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Relationship between strict stationarity and 2nd order stationarity

I've been reading my GARCH class notes and I found some discussions about stationarity. "In Finance, stationarity of order 2 is often considered as more restrictive than the strict stationarity, ...
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What's the difference between interrupted time series analysis and longitudinal analysis via GEE/Mixed-Effects?

What's the difference between interrupted time series analysis and longitudinal analysis via GEE/Mixed-Effects? What may interrupted time series analysis do that GEE fails to do?
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Confused on ARIMA's linearity assumption

The AR(I)MA model or Auto Regressive Integrated Moving Average model is one of the most popular linear models in time series forecasting. In an AR(I)MA model, the future value of a variable is assumed ...
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How we can interpret the behaviour of the residuals in a time series data?

Is there a potential presence of autocorrelation in the error term?
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Time series methods to compare quantitative continuous data (e.g., heart rate) and qualitative/subjective data (e.g., self-reported stress)

I work with a lot of data from sports wearables (e.g., heart rate). I ran a study where people wore a heart rate tracker whilst doing an activity. Every 5 minutes they were prompted to rate how much ...
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Estimating moments of stationary time series — degrees of freedom correction?

I have read that, for a weakly stationary time series we use the empirical moments $$ \hat{\mu} = \frac{1}{T}\sum_{t=1}^T y_t = \bar{y}, \\ \hat{\gamma_0} = \frac{1}{T} \sum_{t=1}^T (y_t-\bar{y})^2, \\...
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How to deal with pulse effect in time series when making future forecast

I am only interested in forecasting correct numbers here. I predict spending among other things. Historically combining exponential smoothing models have worked well for us (errors under 5 percent ...
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How to identify the frequencies of periodic peak signals in a noisy time series? (with R)

Suppose to have two time series with peak signals at different frequencies, like these two: ...
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Reference request - time series analysis book with numerical algorithms

I am working on some applications of time series, and I wanted to find a book that has the numerical algorithms or pseudocode for computing things like AR models, and ARIMA models, using nonlinear ...
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Estimate sales of product from sales of related products

The sales of product I'm interested in published every quarter. The sales of related products published every month. How the sales of the interesting product could be estimated from the sales of ...
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Should the sum of low- and high-pass filtered variances add to the original variance?

I have a time series $f(t)$ of 32 annual values. Doing a frequency analysis, the 3-year period seems predominant. No problem so far. So I want to know how much of the total variance this 3-year ...
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how to z-normalise time series?

Suppose I have 1000 samples of time series, every one of which has 150 points.(If sample frequency is 150 Hz, then every one of the time series stands for 1s.)What is the correct way to z-normalise ...
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Finding the causal representation $x_t = 0.8x_{t-1} - 0.15 x_{t-2} + W_t - 0.30 W_{t-1}$

I'm having troubles finding the causal representation of the ARMA model $x_t = 0.8x_{t-1} - 0.15 x_{t-2} + W_t - 0.30 W_{t-1}$. I began by removing the redundancy in the model such that $\phi(B) = (1-...
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Suggestions for Clustering with Repeating Time-Series Data

I have an idea for a piece of analysis I would like to try, but am not sure how to to prepare my time-series data or whether the answer is an ensemble of cluster analyses (or 'other') It's a simple ...
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How to do time series modelling for different categories?

I have sales data for 200 consecutive days which I can assign to a specific state. My first attempt was to model some NN on and use the first 160 datapoints as train and the rest as my test set. ...
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Backing out an original time series from one that has been converted into day-of-week anomalies from a baseline

Consider the following time series: ...
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Tiime series forecasing methods for small sample

I have real time data source that emits numeric values every 5 seconds. I wanted to raise alert whenever, for example the last 5 consecutive values, deviate more than a certain level. As you can see ...
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Time Series Analysis: Use differencing vs. estimating seasonal patterns with sinusoidal components to remove seasonality

My goal is to remove seasonality of a time series such that I find the underlying stationary process. As far as I can understand, one can remove seasonality in a time series by either differencing w.r....
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decomposing and estimating multiplicative time series

I'm looking through some time series books and looking at different time of models with seasonality we have $X_t = m_t + S_t + Y_t$ or $X_t = m_t*S_t + Y_t$ where $m_t$ is a systematic trend ...
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1answer
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Does multivariate time series forecasting occur in parallel in a Neural Network?

It is common to give some multivariate time series to a Neural Network and get predictions for each individual time series. But my question is, does the NN take all series in consideration when ...
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How to feed LSTM input correctly?

I have a time series problem with 15 minutes as a timestep.The complete data will be from 2016-09-01 00:00:15 to 2016-12-31 23:45:00. I have 5 variables(v1,v2,v3,v4,v5) in the data frame and I want ...
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Check stationary assumption after parameter estimation in ARMA model

I understand that given time series data, say $\{X_t: t=1,\ldots,T\}$, we usually use the Augmented Dickey–Fuller test to verify the stationarity assumption before conducting parameter estimation. ...
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NLP features with nonNLP features (particularly time series features)

Long time statistician, new to nlp here. I have become curious on what is the preferred way to mix nlp features wth non-nlp features. For example, say that I had reason to believe that some sort of ...
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Is it possible to verify correlation between simulated values?

Given the following steps of time series analysis, is it possible to see if the simulations of Principal Components are uncorrelated? obtain a matrix of fairly correlated variables (~20); apply PCA ...
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Component contributions in Additive Model Time Series

I have trained a model for forecasting time series in a greedy procedure: Fit the Trend component T(t) of the series on the original signal y(t) Fit a Cyclical/Seasonal S(t) component of the series ...
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COVID in Germany, LOO-CV for time series

The recent paper in Science [1, 2] infers change points in COVID spread in Germany. The authors fit the number of daily cases assuming one (red), two (orange), and three (green) change points. Every ...
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Statistical Significance of Rolling Z-Score and Z-Score w.r.t. Central Limit Theorem

I have a very fundamental doubt regarding the z-score and rolling z-score method. As per the central limit theorem: http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_Probability/BS704_Probability12....
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Differencing vs detrending and test candidates for Time Series

First time I write here but long since I first got into this interesting forum. Well, I'm getting involved with a Time Series forecasting project and I have a lots of questions in my head... but lets ...
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2answers
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How to calculate correlation on this plot?

How to calculate the correlation between Blue and Red curve. Is the Pearson correlation coefficients works for non-linear data?
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Transformation of disaster data time series for recurrent neural networks

Background: I'm doing an asset pricing project based on this paper https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3350138, where I feed time series of macroeconomic data along with cross-...
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Chow-Lin Time Series Disaggregation

Hy, I am working on a time series with yearly observations starting from 1995. Since I wanted to forecast the next values with ARIMA methods, I thought it was more appropriate to get quarterly data ...
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May I use t-test for time serial dataset compare when residual normality has confirmed?

I have a question about time serial data analysis. I got data which, observed population change through time points. (From T2 ~ T12) What I want is that, comparing population proportion changes ...
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A chart of daily cases of COVID-19 in a Russian region looks suspiciously level to me - is this so from the statistics viewpoint?

Below is a daily chart of newly-detected COVID infections in Krasnodar Krai, a region of Russia, from April 29 to May 19. The population of the region is 5.5 million people. I read about it and ...
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18 views

Reason for I(1) integration order limit in ARDL regression

What is the reason for I(1) integration order limit of independent (or dependent) variable in ARDL regression?, to be specific I(2) variable will 'break' the ARDL model/estimation. Fast thinking I ...
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1answer
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How should I approach (in Python) to detect the change points in following time-series signal?

I want to extract different signals present in this image. To do so, I want to find the boundaries of change point at 2.429 GHz, 2.444 GHz, and so on. Note: These numbers are observed visually and ...
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Simple algorithm to detect change point in time series

Apologies if this question has already been asked, but a lot of similar questions are regarding R or complex algorithms that I don't want. I have numerous 2-dimensional time series, eacch plotting ...
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Bayesian Structural Time Series (R BSTS package): regressors worsen the results. Why?

I am trying to adapt a BSTS model for forecasting with R bsts package. But the results of the BSTS model do not seem right. Adding variables seem to worsen the data results. Am I doing something wrong?...
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Gaussian Mixture Models, application?

I'm analyzing the energy consumption behavior of a population that is increasing monthly (panel data). The population is segmented by both gender and 5 geographical locations. I gather from the data ...
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How to predict next login date & time from the login history of a website

I've been given with login time and date of all the users and I need to predict the next login time and date of each user... Initially, I thought I can group each user's login data and can use linear ...
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R: decompose and consider only random part of the time series for forecasting using forecast package

I have a fundamental question regarding time series forecasting. In forecast package we have different forecasting methods like ThetaJ,TBATS, StructTS etc. Typical or standard approach, as per ...
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How to analysis the trend, seasonality, stationarity of ACF and PACF plots? [closed]

I'm pretty new to time series analysis, I can't tell the characteristics of ACF and PACF plots,can anyone interprets the trend, seasonality, stationarity of the plots given here?
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1answer
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Multiple time series in R

I want to look at the effect of different environmental variables like temperature, precipitation, salinity on abundance of species and I have annual time series data for each variables. So, how can I ...
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1answer
81 views
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Estimating user percentile given avg, min, max for multiple tests

I have data that given me both a user's score for a test, along with the high, average, and low across the class for every test. How would I estimate the user's percentile with this data? We know ...
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How to detect a significant negative shift in a time series plot

Let's say I am monitoring the success rates of a program's data scraping ability for numerous websites and am trying to detect/notice when the rate drops significantly for a certain web site. Rather ...
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0answers
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Predict peaks within a time series

I'm looking for a different approach to linear regression, but I don't know how to model and implement my data. I would like to predict spikes in CPU consumption in a time series using LSTM or newelm. ...
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Is there a way to determine whether there was an unusual change in values in time series?

I have a quarterly sales data set. It is not stationary, nor there is an obvious trend or seasonality. Is there a test kind of like p-value hypothesis testing that checks whether values at the end of ...

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