Questions tagged [unevenly-spaced-time-series]

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

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Creating an index using transactional data

I have a dataset of a series of leases. Variables we have: precinct, date (in quarters), lettable area, price as well as a few others. These assets are not homogenous, although are comparable. In some ...
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13 views

Is it appropriate to interpolate a signal for frequency analysis?

From an experiment, I have (somewhat) irregularly sampled data. The aim is to find the dominant frequency of the signal. As I understand it, most methods for frequency analysis require evenly spaced ...
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94 views

What is the best method to predict the water consumption of EACH customer for the next month?

Say we have a dataset that has the following attributes: customer_id: There are a total of 1000 customers, each of them with a unique customer_id observation_date: The date on which we last observed ...
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30 views

Does it make more logical sense to model the discrete FFT output as a categorical variable or a numerical variable?

I am training a time-series data classifier and some of my features are the output of CT FFT. The results are of course discrete frequencies. I understand that they are in numerical order and higher ...
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30 views

What model for highly irregular time series and very few observations?

I would like to apply an ARIMA model to some data to forecast values into the future. It has to do with bank disbursements. The problem is that, for a normal project, you may have up to 8 observations ...
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Spot unusual patterns in a discrete intermittent time series

I have a multitude of daily time series representing the volume of a certain product arriving per day at a station. There are as many time series as their are stations, and they each look like the ...
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2answers
118 views

Data leakage in temporally overlapping train-test split

Question: In the sliding window train-test split strategy, will there be data leakage if, say, I train on a dataset $X_{t}$ to predict values $y_t$ that were collected after my test data $X_{t+1}$? ...
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17 views

Similarity index between 2 unevenly sampled time series

Say I have 2 time series $s_1$ and $s_2$ with independent variable $x_1$, and dependent value $y$. These 2 series are not evenly sampled across $x_1$, or even sampled at the same rate. Now, I have ...
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23 views

Forecasting with Irregularly Missing Data

Suppose I am supplying $N = 1000$ vendors, and I am looking for a way to predict their demand for my product over $T = 90$ days. Concretely, I hope to take some features for each vendor, such as their ...
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43 views

Event-based time-series analysis

I'm trying to find patterns in a game event that happens randomly over time. The data looks like this: ...
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41 views

Linear Mixed Effects Models - Time dependent covariates with irregular sampling

I've been looking at using a linear mixed effects model to describe the change in a biomarker $y$ over time in a large number of people over a time period $T$ (measured until an event happens). I want ...
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24 views

Equalize multiple unevenly spaced time series for forecasting

I am building a time-series forecasting model to predict some patterns in climatological data. The dataset consists of many (2 mln) time series which look for example as: However the observations ...
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45 views

Comparing time series data with different interval

I have time series data for one day (temperature) measured every 5 seconds and would like to increase the period between measurements and somehow calculate the level of error/similarity/correlation (I'...
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1answer
28 views

How to detect a drop in regularity / increase in spontaneity of time data?

I have been tasked with detecting changes in regularity for multiple datasets. Each dataset is linked to a different type of event, and each dataset consists of a few hundred timestamps representing ...
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2answers
61 views

Correlation between two time series of very different frequency?

I am wondering if there is a limit to calculation of the correlation between two vectors symbolizing events of VERY different frequency. A = events that can appear in the millisecond area B = events ...
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31 views

multi-step forecast classification problem

There are multiple approaches for multi-step time series forecasting, mainly recursive strategy, independent and MIMO. If I want to predict whether an event is going to occur at some point in the ...
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34 views

Time series “window” terminology

I've been reading about time series analysis/prediction and there're some things about its terminology that aren't still clear to me. For the following definitions, suppose that we're only interested ...
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23 views

Customer next visit behavior forecast

I'm currently working with retail data about a store and the goal is to predict when each customer will visit the store again e.g customer id = 1 will probably visit again in 6 days(recency) My ...
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20 views

Modeling unequally spaced time series as OU process

I have a time series wherein the data points are unevenly spaced. I read this can be modeled as discrete time observation from a continuous time process. So I am trying to model it as an Ornstein-...
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1answer
52 views

Scale of time covariate in `corCAR1()` matters?

I'm running a bunch of GAM analyses on time series data (measurements of my own weight). Unlike many examples I see online, my data is not spaced evenly in time. In fact, time points can come minutes ...
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37 views

Forecasting using unevenly sampled data

We have some head position data obtained from the sensors of a Virtual Reality headset. First 200 ms of the data from one user measured while watching one 360-video sequence looks like as follows: <...
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2answers
143 views

How to forecast number of event occurences in a certain time period?

I am attempting to predict how many times a certain event will happen in a time period. For instance, predict the number of time the event will occur in the next 5 hours. I have data going back a ...
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1answer
16 views

Mean time to event with uneven follow up times

I'm trying to compare the effect of two treatments on the time to fracture healing, and came across a study that does the following: They compared effect of treatment A vs treatment B on fracture ...
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17 views

Between Group comparison for parameters measured on different time points

I have a dataset of 100 patients for whom data was recorded of 3 different parameters say Heart rate, temperature and ETCO2 levels which are measured in 3 phases eg. Phase 0, Phase 1 and Phase 2. The ...
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1answer
27 views

Tackling challenging semi-weekly or weekly data with gaps

I am working on ferry itineraries (i.e. tickets sold in a specific itinerary). Consider the scenarios: 1) itinerary taken only on Tuesdays and Thursdays every week ( but with gaps - depending on ...
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74 views

Missing as opposed to non existent data in time-series forecasting

Suppose you have a set of observations that occur at regular intervals in time, but containing regular gaps during which there is no data, not because it is hidden or missing, but because the ...
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118 views

Handling non-uniform frequency data

I have some medical data (heart rates) at non-uniform intervals (usually readings every few min at the start of the study and several readings a minute toward the end). The timing and when the change ...
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1answer
275 views

Notation for unevenly-spaced to evenly-spaced time series conversion

I have an unevenly-spaced time series. To make it evenly-spaced, I resample the time series to a larger timespan (e.g. day) and sum up all values within this time frame. In Python (and pandas) it is ...
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199 views

How to deal with unevenly spaced time data in time series linear regression

I have a dataset that has a number of instances that look like the following: ...
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1answer
101 views

Practical handling of Dynamic Time Warping for time-series with unequal sampling frequency (irregular time series)

Hi all and thank you for taking time to read this. When I read the general literature about dynamical time warping (and the vignette for the $\texttt{R}$ package $\texttt{dtw}$), the time series seem ...
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24 views

Representing Multiple Variables Measured at Multiple Independent Times

I'm attempting to find a good storage/data representation in "R" for a data set with a large number of variables, each of which is measured independently for a number of years but at irregular ...
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1answer
55 views

How to calculate correlation for 2 time series where the tick times do not line up

I'm looking to compute intraday correlation for 2 tick by tick intraday commodity price time series. The tick times do not line up across the 2 time series. I'm thinking of converting the time ...
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1answer
98 views

How to check if a data is poisson sampled?

I was reading one article which develops a theory for the Poisson sampled data. That is the data is collected over time-points $\{T_k, k>1\}$, which are jump-moments of a homogeneous Poisson ...
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1answer
193 views

How to model time series with unevenly spaced observations?

I have three time series of the following form: $T = T_{2000}, T_{2004}, T_{2008}, …$ $U = U_{1998}, U_{1999}, U_{2000}, U_{2001}, U_{2002}, U_{2003}, …$ $V = V_{1998}, V_{1999}, V_{2000}, V_{2001},...
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43 views

Classify time series with unequal lenghts [closed]

I have a set of time series sensor measurements (acceleration and gyroscope readings) for driving events (harsh acceleration, harsh brake …) with the type, start and end of each event. I need to ...
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108 views

Why ARMA models works on assumption of uniformly sampled time series?

I am dealing with irregularly spaced time series data. I have been reading papers how ARMA based approaches can be used for time series forecasting. However, ARMA assumes uniformly sampled time ...
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59 views

Smoothing with non-regular observations

If we have data that changes continuously in time, and we sample this data at regular intervals - i.e. we get samples $x_0$, $x_1$, $\dots$, where the time $\Delta t$ between taking samples $x_i$ and $...
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3answers
580 views

Binary target prediction using LSTM with sparse events in time

I have a data of patients that have multiple events happening in there medical history, I'd like to predict a target of having a specific targeted-event in the next 30 days. The data is timestamped ...
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1answer
283 views

R: generalised additive model on proportional data

Introduction I am analysing temporal population data on the amphipod Orchestia gammarellus. At several moments each year, all animals were collected from a small spot, and several life history traits ...
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1answer
95 views

Machine Learning techniques for uneven series of events

I don't know the correct terminology and so long everything I typed into google lead me to some version of time series modeling where all time series had the same number of points in a given time ...
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1answer
578 views

Detecting leading stocks using lag correlation

I am working on a project to find leading stocks in a stock market by using lag correlation. Say I want to compare 2 stocks, X and Y, and I have the time series of stock prices. Assume that the time ...
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2answers
55 views

Example of dataset where the data collected at time-points $g(t_1), g(t_2), \ldots$

What would be some practical scenarios where we collect data at time-points $g(t_1), \ldots, g(t_n)$, where $g$ is an increasing function? For example, $g(t) = \exp(t)$ or $\ln t$. To be more clear, ...
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1answer
853 views

LSTM - random and always-different time between data measurements?

I am working with a time series problem where the time between two data measurements is random, and I am trying, without luck, to find an LSTM architecture that can handle this. A very simplified ...
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271 views

Calculating variance and standard deviation of continuous time series

I am trying to calculate the variance and standard deviation of an unevenly spaced continuous time series. Example data: ...
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2answers
66 views

Forecast sales and then ungroupto find individual sales

I am trying to solve a problem for a brewery: A brewery has 50 beer types in total out of which only 8 to 10 beers are available on tap for a single day i.e only 8 to 10 beers will be sold on any ...
2
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2answers
639 views

Dealing with missing data in Time Series or non-constant time intervals for forecasting in R (ARIMA, Holt Winters, Theta)

I have a time series of sensor data from a machine. This machine is sometimes moved and thus there are big chunks of missing data, here is a plot of the data points: My goal is to try to start ...
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0answers
45 views

Machine Learning on Sequential Data Reperesenting Events

Would you please recommend which model should I use for modeling user activity observing certain events that happen to them in time. I want to make sense of the history of events (just a presence of ...
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1k views

How to find out if two time series correlated? they are of not equal length and with unknown delay between cause and effect, irregular log time?

Writing the whole problem, to avoid asking a question when in reality I needed an answer to a whole different question, and just didn't know how to ask. I have a lot of data about my daily life and I ...
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318 views

Forecasting with ARIMA models and sensor data in R

I'm trying to apply ARIMA models to sensor data and would be thankful if anyone could answer my questions. I should add that I have very little experience with time series (trying to change that). ...
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34 views

Comparing rates of change with unequal and unevenly spaced timepoints

I am trying to compare rates of change in my data and although the units of time are the same, some instances have more/less time points than others and the time points are not uniform. Is there some ...