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

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Do we have a problem of “pity upvotes”?

I know, this may sound like it is off-topic, but hear me out. At Stack Overflow and here we get votes on posts, this is all stored in a tabular form. E.g.: post id voter id vote type ...
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11answers
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Simple algorithm for online outlier detection of a generic time series

I am working with a large amount of time series. These time series are basically network measurements coming every 10 minutes, and some of them are periodic (i.e. the bandwidth), while some other ...
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Pitfalls in time series analysis

I am just starting out self-learning in time series analysis. I've noticed that there are a number of potential pitfalls that aren't applicable to general statistics. So, building on What are common ...
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Time series 'clustering' in R

I have a set of time series data. Each series covers the same period, although the actual dates in each time series may not all 'line up' exactly. That is to say, if the Time series were to be read ...
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Is it possible to do time-series clustering based on curve shape?

I have sales data for a series of outlets, and want to categorise them based on the shape of their curves over time. The data looks roughly like this (but obviously isn't random, and has some missing ...
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10answers
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Books for self-studying time series analysis?

I started by Time Series Analysis by Hamilton, but I am lost hopelessly. This book is really too theoretical for me to learn by myself. Does anybody have a recommendation for a textbook on time ...
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5answers
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Efficient online linear regression

I'm analysing some data where I would like to perform ordinary linear regression, however this is not possible as I am dealing with an on-line setting with a continuous stream of input data (which ...
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6answers
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Is there any gold standard for modeling irregularly spaced time series?

In field of economics (I think) we have ARIMA and GARCH for regularly spaced time series and Poisson, Hawkes for modeling point processes, so how about attempts for modeling irregularly (unevenly) ...
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How to make a time series stationary?

Besides taking differences, what are other techniques for making a non-stationary time series, stationary? Ordinarily one refers to a series as "integrated of order p" if it can be made stationary ...
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Getting seRious about time series with R

If you think back, to when you first started with time series analysis. What tools, R packages and internet resources do you wish you had known about? What I'm trying to ask is, where should one ...
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2answers
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Proper way of using recurrent neural network for time series analysis

Recurrent neural networks differ from "regular" ones by the fact that they have a "memory" layer. Due to this layer, recurrent NN's are supposed to be useful in time series modelling. However, I'm not ...
16
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Can data cleaning worsen the results of statistical analysis?

An increase in the number of cases and deaths occurs during epidemics (sudden increase in numbers) due to a virus circulation (like West Nile Virus in USA in 2002) or decreasing resistance of people ...
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Ways to reduce high dimensional data for visualization

I'm working on a 2D physical simulation and I am collecting data in time at several points. These discrete points are along vertical lines, with multiple lines in the axial direction. This makes the ...
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5answers
965 views

Data has two trends; how to extract independent trendlines?

I have a set of data that is not ordered in any particular way but when plotted clearly has two distinct trends. A simple linear regression would not really be adequate here because of the clear ...
16
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1answer
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How to apply Neural Network to time series forecasting?

I'm new to machine learning, and I have been trying to figure out how to apply neural network to time series forecasting. I have found resource related to my query, but I seem to still be a bit lost. ...
16
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3answers
953 views

Logistic Regression and Dataset Structure

I am hoping that I can ask this question the correct way. I have access to play-by-play data, so it's more of an issue with best approach and constructing the data properly. What I am looking to do ...
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9answers
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Time series for count data, with counts < 20

I recently started working for a tuberculosis clinic. We meet periodically to discuss the number of TB cases we're currently treating, the number of tests administered, etc. I'd like to start ...
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3answers
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Determining whether a website is active using daily visits

Context: I have a group of websites where I record the number of visits on a daily basis: ...
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5answers
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Seeking certain type of ARIMA explanation

This may be hard to find, but I'd like to read a well-explained ARIMA example that uses minimal math extends the discussion beyond building a model into using that model to forecast specific cases ...
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4answers
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Period detection of a generic time series

This post is the continuation of another post related to a generic method for outlier detection in time series. Basically, at this point I'm interested in a robust way to discover the ...
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Difference between longitudinal design and time series

What is/are the difference(s) between a longitudinal design and a time series?
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1answer
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STL trend of time series using R

I am new to R and to time series analysis. I am trying to find the trend of a long (40 years) daily temperature time series and tried to different approximations. First one is just a simple linear ...
14
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2answers
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Testing significance of peaks in spectral density

We sometimes use spectral density plot to analyze periodicity in time series. Normally we analyze the plot by visual inspection and then try to draw a conclusion about the periodicity. But has the ...
14
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2answers
737 views

Question about logistic regression

I want to run a binary logistic regression to model the presence or absence of conflict (dependent variable) from a set of independent variables over a 10 year period (1997-2006), with each year ...
14
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Choosing seasonal decomposition method

Seasonal adjustment is a crucial step preprocessing the data for further research. Researcher however has a number of options for trend-cycle-seasonal decomposition. The most common (judging by the ...
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3answers
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What are disadvantages of state-space models and Kalman Filter for time-series modelling?

Given all good properties of state-space models and KF, I wonder - what are disadvantages of state-space modelling and using Kalman Filter (or EKF, UKF or particle filter) for estimation? Over let's ...
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1answer
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Eigenfunctions of an adjacency matrix of a time series?

Consider a simple time series: > tp <- seq_len(10) > tp [1] 1 2 3 4 5 6 7 8 9 10 we can compute an adjacency matrix for this time series ...
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Cross-validating time-series analysis

I've been using the caret package in R to build predictive models for classification and regression. Caret provides a unified interface to tune model hyper-parameters by cross validation or boot ...
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2answers
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Using deep learning for time series prediction

I'm new in area of deep learning and for me first step was to read interesting articles from deeplearning.net site. In papers about deep learning, Hinton and others mostly talk about applying it to ...
13
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2answers
483 views

Does a cointegration model exist for irregularly spaced time series?

It isn't clear to me how to calculate cointegration with irregular time series (ideally using the Johansen test with VECM). My initial thought would be to regularize the series and interpolate ...
13
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1answer
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Usage of HMM in quantitative finance. Examples of HMM that works to detect trend / turning points?

I am discovering the marvellous world of such called "Hidden Markov Models", also called "regime switching models". I would like to adapt a HMM in R to detect trends and turning points. I would like ...
13
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702 views

Fitting an ARIMAX model with regularization or penalization (e.g. with the lasso, elastic net, or ridge regression)

I use the auto.arima() function in the forecast package to fit ARMAX models with a variety of covariates. However, I often have a large number of variables to select from and usually end up with a ...
13
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2answers
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Logistic regression for time series

I would like to use a binary logistic regression model in the context of streaming data (multidimensional time series) in order to predict the value of the dependent variable of the data (i.e. row) ...
13
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1answer
356 views

How to check which model is better in state space time series analysis?

I am doing time series data analysis by state space methods. With my data the stochastic local level model totally outperformed the deterministic one. But the deterministic level and slope model gives ...
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R code for time series forecasting using Kalman filter

Does anybody have a good example for Time Series Forecasting/smoothing using Kalman Filter in R?
12
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438 views

Using time series analysis to analyze/predict violent behavior

This is a bit of a flippant question, but I have a serious interest in the answer. I work in a psychiatric hospital and I have three years' of data, collected every day across each ward regarding the ...
12
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2answers
304 views

What is this permutation?

I was recently looking for ways to resample time series, in ways that Approximately preserve the auto-correlation of long memory processes. Preserve the domain of the observations (for instance a ...
12
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1answer
565 views

How to predict one time-series from another time-series, if they are related

I have been trying to solve this problem for over a year without much progress. It is part of a research project I'm doing, but I will illustrate it with a story example I made up, because the actual ...
12
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2answers
278 views

Irregularly spaced time-series in finance/economics research

In financial econometrics research, it is very common to investigate relationships between financial time series that take the form of daily data. The variable will often be made $I(0)$ by taking the ...
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Mixed model vs. Pooling Standard Errors for Multi-site Studies - Why is a Mixed Model So Much More Efficient?

I've got a data set consisting of a series of "broken stick" monthly case counts from a handful of sites. I'm trying to get a single summary estimate from two different techniques: Technique 1: Fit a ...
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5answers
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What method can be used to detect seasonality in data?

I want to detect seasonality in data that I receive. There are some methods that I have found like the seasonal subseries plot and the autocorrelation plot but the thing is I don't understand how to ...
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3answers
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Good introductions to time series (with R)

I am currently collecting data for an experiment into psychosocial characteristics associated with the experience of pain. As part of this, I am collecting GSR and BP measurements electronically from ...
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3answers
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How to use DLM with Kalman filtering for forecasting

Could someone walk me through an example on how to use DLM Kalman filtering in R on a time series. Say I have a these values (quarterly values with yearly seasonality); how would you use DLM to ...
11
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4answers
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Why does a time series have to be stationary?

I understand that a stationary time series is one whose mean and variance is constant over time. Can someone please explain why we have to make sure our data set is stationary before we can run ...
11
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3answers
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How to measure smoothness of a time series in R?

Is there a good way to measure smoothness of a time series in R? For example, -1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1.0 is much smoother than ...
11
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3answers
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Application of wavelets to time-series-based anomaly detection algorithms

I've been beginning to work my way through Statistical Data Mining Tutorials by Andrew Moore (highly recommended for anyone else first venturing into this field). I started by reading this extremely ...
11
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3answers
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Robust outlier detection in financial timeseries

I'm looking for some robust techniques to remove outliers and errors (whatever the cause) from financial time-series data (i.e. tickdata). Tick-by-tick financial time-series data is very messy. It ...
11
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4answers
803 views

Statistical similarity of time series

Supposing one has a time series from which one can take various measurements such as period, maximum, minimum, average etc. and then use these to create a model sine wave with the same attributes, are ...
11
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2answers
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Getting started with neural networks for forecasting

I need some resources to get started on using neural networks for time series forecasting. I am wary of implementing some paper and then finding out that they have greatly over stated the potential of ...
11
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2answers
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What statistics are preserved under aggregation?

If we have a long, high resolution time series, with lots of noise, it often makes sense to aggregate the data to a lower resolution (say, daily to monthly values) to get a better understanding of ...