Questions tagged [unevenly-spaced-time-series]

Time series sampled or measured at unevenly (or irregularly) distributed time points.

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Forecasting Square Waves

I am involved in a social experiment with other college students. The experiment involves simulating current price of the market at which a given asset can be bought or sold for immediate delivery. We,...
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How to decompose irregularly spaced count data?

My advisor would like to do a time series analysis on our data, but I'm not sure if it qualifies as a true "time series" dataset. The data collected are fish counts and during collection we ...
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Impulse response function for discontinous time series

I have monthly time series on forecasts (for the months of August, September, October, November, December, and January.) The data is only available for these months and doesn't exist for other months. ...
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Autocorrelation of discontinuous time series data [closed]

I am attempting to perform an autocorrelation study using python on a discontinuous time series dataset. To share a bit about how my data looks like, it is a single column of values, which spans over ...
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Research field of timeseries with unpaired measurements?

I have measurements of cells' gene expression at different time points (t0, t1, t2, ...). Cells die when measured and therefore all measurements are from different samples, i.e. I do not observe any ...
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Estimating covariance matrix of irregularly updated time series

I would like to estimate the covariance matrix of returns from a set of time series that don't get updated regularly. To be precise, in my case all of the series fall into 2 classes. Class A gets ...
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How to correctly get the error of a timeseries mean, when each point in the timeseries has errors?

I have time-series data (brGDGT samples from a core with an age-depth model), and each data point in the time-series has an error associated with it as per the dummy table below. time value plus ...
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Model with time as predictor but some rows have large time span

I want to model a linguistic feature (number of a certain word at the beginning of the sentence) through centuries. I am familiar with logistic regressions, Poisson regressions and GAMM to model such ...
Giuseppe Magistro's user avatar
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In prediction modelling, is it bad practice to combine differing sampling frequencies of covariates into the same model?

An example of this type of prediction task is modelling economic time-series data. Depending on the type of data being reported, the sampling frequency varies: GDP is reported quarterly, employment ...
ron burgundy's user avatar
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Is it reasonable to calculate a polynomial regression using days but showing month as on plot?

My data concern vegetative relative cover of nettle plants recorded at up to thirty sites at irregular intervals over a calendar year (17/1/2009 - 16/1/2010) and different dates for different groups ...
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Handling Mixed-Frequency time series data for Feature Selection

I'm currently working on a project where I aim to apply LASSO regularization and conduct variable importance analysis on WTI crude oil prices. My challenge is dealing with datasets that have different ...
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What is the best way to measure time series similarity with these features?

I have a time series data from a wearable device. I have two time series as arrays df1.VALUE1 and df2.VALUE1. I would like to to measure similarity of these arrays. These arrays have different ...
dsapprentice's user avatar
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How to Estimate Growth Rates Between Two Periods from Data with Varying Time Intervals Between Measurements?

I have a dataset of sales records for properties in a local area spanning over 20 years. Each property has one or more historical records of sale, however, as the sales do not occur every year, there ...
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Predicting a variable from categorical time series input that is unevenly spaced and of unequal length

I would like to predict the value of some variable $x$ at time $t$ based on the evolution of the state of the system in the time interval $(t-T,t]$. The system can be in any number of states in the ...
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Notable changes when modeling unevenly univariate spaced time series as an evenly spaced multivariate time series?

When attempting to model univariate data (although, this could easily be extended to the multivariate case) that is unevenly spaced over time, a natural approach to be able to apply common time series ...
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Autocorrelation and other problems modeling pupillometry with bam() from mgcv

TLDR: Model shows autocorrelation and non-normality in the residual plots even after AR1 and attempting to control for autocorrelation in parameters; I have a non-continuous time variable (it makes ...
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Spectral analysis of timestamps

I have a dataset that contains only the timestamps of occurring events, i.e. I have for ~10^5 events the millisecond-precise time when the respective event happened (i.e. the data is unary, containing ...
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Formatting cox data with time-dependent continuous variables and unequal time intervals

I am planning to do a joint model for longitudinal and survival data, with multiple time-dependent/time-varying continuous variables. However, I am having trouble re-formatting my dataset to be in a ...
Lily Vi's user avatar
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What survival model can handle a different time for each covariate?

I would like to fit something like a Cox model but each covariate on my data could have been measured at a different time, different for each person. Is there any alternative survival model allowing ...
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Analysing short time sequences for many subjects

I have data on ~100 subjects: blood values taken on different days (day 1, 4, 7, and 11). The subjects undergo different treatments (but only one treatment per subject) and may develop different ...
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Flat window removal from time series

I have a time series that I'm using for forecasting and I'm facing an issue with a flat period. In my time series, I have the following dynamic: In the past the quantity was stationary (red part), ...
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Extension of Markov Model with Time Dependence and Emissions

I am interested in a continuous time system with $n$ observable factors $s_1(t),\ldots,s_n(t)$ and a discrete process $z(\tau_i)\in\{0,1\}$ with random emission times $\tau_1,\ldots,\tau_m$. The ...
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Aggregating physical science data from multiple sources for time series

I’m attempting to build a time series model using water quality data. The problem is, for any given site where this data exists, it is irregular time intervals and sparse data (anywhere from 20-70% ...
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Multi-variate multistep time series Forecasting for non-stationary data

The problem is that I have a very special time series. It is sensors data for a machine. I have about 400 sensor data which I want to use to forecast the machine advancing speed. The data contain a ...
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GAM model parameter estimation problems

I have been trying to develop a GAM to predict future subsidence from previous subsidence data. I am new to GAM and have based my code on [this].1 The data looks like this: ...
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Can you use Dynamic Factor Analysis with time-series datasets that cover different time periods?

I have time series data for several sites and I'm interested in looking at whether the changes in covariates at these sites vary in a consistent manner over the time series. I thought dynamic factor ...
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Daily data shows seasonality when averaged across month. How to adjust on a daily basis?

I have match data across multiple years. I've collected the data in the form Date Total Points Scored Now here are some issues: (i) since this is match data, it ...
TweetyTwarty's user avatar
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Mixed model for repeated measures with different timepoints and intervals

is there a way to design a mixed model for uneven number of measurements per subject and (more importantly) with uneven time intervals between measurements which are taken at different time points (...
opiczak's user avatar
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How to weigh time-series data with a utility function with jumps?

I have data measured at fixed intervals in one process. It needs to be weighed with cost value that is generated in another process that is analogue and the costs it generates have jumps. Imagine a ...
Boppity Bop's user avatar
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How to incorporate known future target values in prediction model?

Imagine that we collect time series data from a sensor. Due to measurement errors and let's say power outages, there are gaps in the time series that can be quite long. For example if data would be ...
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When do ARMA models fail?

I have just started learning about Autoregressive–moving-average model (ARMA). On the Wiki page, it has been mentioned that: ARMA is appropriate when a system is a function of a series of unobserved ...
Omar Shehab's user avatar
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Hypothesis testing on a moving-average model on unevenly-spaced time series

I have some irregular time series $X$, where $X_t$ are identically distributed, and bounded between 0 and 1. I perform a moving average $Y_t = \sum_{t'} \frac{w(t-t')}{\sum_k w(t-k)}X_{t'}$. The ...
spierenb's user avatar
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1 answer
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What is this type of data called?

An event occurs once per period, such as once per year. Time is measured in discrete units, such as days of the year. Let $A_y$ be the day in year $y$ on which this event occurs. However, we do not ...
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Lagged predictors in irregular-time / asynchronous / time-unconstrained data

In growth curve modeling or other approaches, when time is constrained/synchronous/regular (i.e. panel/wave data; all observations occur synchronously), lagged prediction is trivial - simply add t-1 ...
Daniel B's user avatar
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1 answer
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Extract features of uneven time series and use it in cross-sectional study

I have a data like below subj_1 = 10,20,15,30,60,70,90 (in resolution of years - 2011 to 2018) subj_2 = 10,20,30,40 (in resolution of months - Jan 2011 to Apr 2011) subj_3 = 10,15,20,30,45,55,60,70,90,...
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How can I generate probabilistic forecasts to do probabilistic classification?

I have a collection of univariate, irregularly spaced, financial time series. Each series is labeled by its class. The image below shows some example data. A note on the data: The time series could ...
Escherichia's user avatar
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Controlling for time elapsed between baseline and treatment

I'm having a mixed model with 5 repeated measurements (time is categorical because the timepoints represent discrete events). Edit: I have 5 meaningful timepoints: t0 baseline (neutral timepoint); t1 ...
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Short Time Series Granularity of Timestamps

I have to make a prediction on a time series whereby there are 10 years of data. My first option is to predict the values aggregated to the annual level using SARIMAX (excluding the seasonality ...
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1 answer
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Interrupted Time Series with Unevenly Distributed Samples

I'm working on causal inference using Interrupted Time Series Design. I have multiple samples per day and am selecting my analysis bandwidth based on pre-treatment RMSE on leave-on-out cross ...
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Forecasting Prices with interdependence that form a Timeseires

I have already asked a simmilar question, but i thoguth that this was not phrased well and hence i am trying a new post were i ask a better question. Let me know if this is ok. Judging by some of the ...
NorrinRadd's user avatar
3 votes
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137 views

Unevenly spaced multiple time series modelling

In the context of unevenly spaced (multiple) time series (USTS), are there any classical approaches? If they were evenly spaced, we would try ARIMA, or VAR, or even State-Space models. I've been ...
An old man in the sea.'s user avatar
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causal effects when y is continuous and discontinuous over time

I am interested in estimating the causal effects of A(treatment) on Y(outcome). Here treatment is a binary variables (yes vs no). Outcome is a continuous variable(weight), and this is normalized to Z ...
Ahir Bhairav Orai's user avatar
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Regression for hierarchical time series

I have a dataset with time series A, B and C from different countries. These time series have different starting points, missing values, and irregular intervals (only weekdays). Instead of forecasting,...
Max J.'s user avatar
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How to get daily returns from irregularly spaced price time-series?

I have a timeseries that has irregularly spaced time indices as below ...
PyRsquared's user avatar
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Feature creation based on irregular time series with strictly monotonic values

I am given the following problem to run predictive maintenance on the following mess: I have some IoT sensors out in the field, but I cannot easily change the software on them. Also, data collection ...
Frank's user avatar
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Unevenly spaced time-series forecasting and anomaly detection for an industrial usecase

I am currently working on a PhD project for a car manufacturing company, which basically consists of creating a predictive maintenance application for the machines that are currently used to fill the ...
iotx's user avatar
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How to create an ML training dataset from unevenly spaced multivariate timeseries?

I have a time series dataset with multiple features X_n from which I want to predict an output y. However, both the x and y values are unevenly spaced and were sometimes collected at different ...
Ludo's user avatar
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Predicting events - seizures in epilepsy. A question about time series models matching with observations

I've been keep a diary of epilepsy seizures, and would like to attempt prediction modelling as an help for better management of anti consultant therapy. Could you help to suggest models that fit with ...
user305883's user avatar
1 vote
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How to analyse simple internet downtime log?

I have a log of internet downtime (timestamp + downtime in seconds) and I'd like to analyse it to try and spot trends, especially if I can see that it normally happens around specific times of the day,...
Martin's user avatar
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Cross Correlation between highly unevenly spaced time series

I have the following problem. I have 2 time series, one on a yearly basis (2010 - 2020) and one on a daily basis (01/01/2010 - 31/12/2020). I looking for methods how I can investigate the correlation ...
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