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

learn more… | top users | synonyms

45
votes
13answers
20k views

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 ...
45
votes
3answers
1k views

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 ...
36
votes
15answers
18k views

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 ...
33
votes
7answers
2k views

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 ...
30
votes
3answers
6k views

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 ...
29
votes
5answers
4k views

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 ...
28
votes
6answers
12k views

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 ...
27
votes
5answers
14k views

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 ...
27
votes
4answers
6k views

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 ...
26
votes
1answer
16k views

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. ...
26
votes
5answers
12k views

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 ...
26
votes
2answers
7k views

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 ...
23
votes
4answers
2k 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 ...
23
votes
3answers
1k views

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 ...
23
votes
6answers
3k views

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) ...
22
votes
5answers
7k views

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 ...
22
votes
4answers
10k views

Difference between longitudinal design and time series

What is/are the difference(s) between a longitudinal design and a time series?
19
votes
4answers
23k views

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 ...
19
votes
3answers
3k views

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 ...
19
votes
5answers
2k views

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 ...
18
votes
1answer
8k views

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 ...
17
votes
2answers
1k 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 ...
17
votes
2answers
1k views

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 ...
16
votes
5answers
944 views

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 ...
16
votes
5answers
23k views

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 ...
16
votes
9answers
3k views

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 ...
16
votes
5answers
4k views

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 ...
16
votes
3answers
1k 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 ...
15
votes
5answers
4k views

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 ...
15
votes
4answers
11k views

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?
15
votes
2answers
1k views

Why are “time series” called such?

Why are “time series” called such? Series means sum of a sequence. Why is it time Series, not time sequence? Is time the independent variable?
15
votes
3answers
13k views

How to fit an ARIMAX-model with R?

I have four different time series of hourly measurements: The heat consumption inside a house The temperature outside the house The solar radiation The wind speed I want to be able to predict the ...
15
votes
4answers
3k views

Features for time series classification

I consider the problem of (multiclass) classification based on time series of variable length $T$, that is, to find a function $$f(X_T) = y \in [1..K]\\ \text{for } X_T = (x_1, \dots, x_T)\\ ...
15
votes
3answers
5k views

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 ...
15
votes
3answers
507 views

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: ...
15
votes
1answer
5k views

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) ...
14
votes
3answers
13k views

Difference between forecast and prediction?

I was wondering what difference and relation are between forecast and prediction? Especially in time series and regression? For example, am I correct that: In time series, forecasting seems to mean ...
14
votes
4answers
9k views

When to log transform a time series before fitting an ARIMA model

I have previously used forecast pro to forecast univariate time series, but am switching my workflow over to R. The forecast package for R contains a lot of useful functions, but one thing it doesn't ...
14
votes
2answers
7k views

How to find a good fit for semi-sinusoidal model in R?

I want to assume that the sea surface temperature of the Baltic Sea is the same year after year, and then describe that with a function / linear model. The idea I had was to just input year as a ...
14
votes
2answers
2k views

Analysis of time series with many zero values

This problem is actually about fire detection, but it is strongly analogous to some radioactive decay detection problems. The phenomena being observed is both sporadic and highly variable; thus, a ...
14
votes
1answer
3k views

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 ...
14
votes
1answer
7k views

How to decompose a time series with multiple seasonal components?

I have a time series that contains double seasonal components and I would like to decompose the series into the following time series components (trend, seasonal component 1, seasonal component 2 and ...
14
votes
2answers
1k 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
votes
2answers
3k views

Real-life examples of moving average processes

Can you give some real-life examples of time series for which a moving average process of order $q$, i.e. $$ y_t = \sum_{i=1}^q \theta_i \varepsilon_{t-i} + \varepsilon_t, \text{ where } \varepsilon_t ...
14
votes
1answer
364 views

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 ...
14
votes
1answer
489 views

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 ...
14
votes
1answer
470 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 ...
13
votes
3answers
3k views

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 ...
13
votes
2answers
3k views

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 ...
13
votes
2answers
638 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 ...