All Questions
6,764 questions with no upvoted or accepted answers
20
votes
0
answers
2k
views
Implementation of CoVaR (a systemic risk measure) in R
I'm trying to estimate CoVaR using bivariate DCC GARCH in R. The concept of CoVaR is the dependence adjusted of VaR, which was first introduced by Adrian and Brunnermeier (2011). However, this ...
- 275
17
votes
0
answers
14k
views
Time series regression with overlapping data
I am seeing a regression model which is regressing Year-on-Year stock index returns on lagged (12 months) Year-on-Year returns of the same stock index, credit spread (difference between monthly mean ...
- 338
16
votes
0
answers
427
views
What is tantile regression?
My question follows on this discussion of medials and tantiles vs medians and quantiles from earlier this year:
When would we use tantiles and the medial, rather than quantiles and the median?
As ...
- 10.9k
14
votes
0
answers
687
views
Convolutional neural network for multi-variate time series?
I want to use CNN architectures for classification of multivariate time-series, where we apply one label to each sequence.
I searched the net for the available designs in the literature and i found ...
- 439
13
votes
0
answers
392
views
Is autocorrelation not worth addressing with small N?
Consider a simple regression context in which there is a small set of response values, $Y$, and corresponding dates, $X$. (For simplicity, we can assume the dates are equally spaced.) We would like ...
12
votes
0
answers
2k
views
Empirical Prediction interval for time series forecast based on quantile regression
As Gardner notes "almost all point forecasts are wrong", so prediction intervals (PI) are necessary to quantify uncertainty and help us make informed decisions. There exists theoretical PI, and in ...
- 8,655
11
votes
3
answers
2k
views
Need advice on change point (step) detection
I have a time series with lots of steps/jumps (data file here). A plot is given below. I would like to subtract an appropriate value for each of these square wave features to bring them back down to ...
- 301
10
votes
0
answers
5k
views
Stationary vs Stability
I am searching for an example of an unstable VAR($p$) process (its reverse characteristic polynomial has no roots inside and on the complex unit circle) which is stationary. I come up with this ...
- 1,370
9
votes
1
answer
2k
views
PyMC3 implementation of Bayesian MMM: poor posterior inference
Google released a whitepaper on Media Mix Modelling (MMM) in 2017; vanilla MMM (established in the 1960s) uses multivariate regression. It's a decent mechanism to understand which of your marketing ...
- 3,520
9
votes
0
answers
948
views
Why does uncertainty of the autocorrelation coefficient increase as lag increases?
The Python module statsmodels contains functions for ACF and PACF. Below is an example from the docs with a plot that shows the (zero-centered) confidence ...
- 967
9
votes
1
answer
149
views
Is there a ML or DL tool that can learn to detect periodically occurring patterns in a one dimensional time series?
I am trying to create a tool that labels refrigerator temperature readings. A reading is taken every 5 minutes, and its label identifies whether of not it was taken while the refrigerator was ...
- 91
9
votes
0
answers
1k
views
Should I cluster my standard errors even when using a multilevel model?
I've been reading up on multilevel modeling, and have noticed that many sources seem to frame it as an "alternative" to using cluster-robust standard errors.
My question: Are they really alternatives?...
- 91
9
votes
0
answers
2k
views
When and why do I have to use "trait" for multinomial multilevel models with MCMCglmm in R?
I want to estimate a multilevel multinomial logit model but I am struggling with the terminology and notation used by the R-package MCMCglmm. There is documentation ...
9
votes
0
answers
1k
views
Fisher's test of periodicity
I have evenly sampled time series on which I applied Fourier transform. I am trying do determine if the signal contains statistically significant periodic components. I have succeeded with determining ...
- 231
9
votes
1
answer
831
views
Comparing coefficients in multilevel models
Is it meaningful to compare the coefficients of two different predictors in multilevel model when the two are at different levels?
Specifically I have two variables which measure the same construct ...
- 1,039
8
votes
0
answers
7k
views
What is difference between interrupted time series and regression discontinuity design
Say that one has data over time, t, on an outcome, y. There is an event that happens at t==0....
- 347
8
votes
0
answers
226
views
Regression with dependent data with low dependence
Suppose you have data that is grouped in one way or another and therefore the assumption of independence is suspect. But you look at the intraclass correlation (or autocorrelation) and it is very ...
- 128k
8
votes
0
answers
273
views
Time series: sample vs. population + population vs. realizations of random process
Suppose we have $120$ monthly observations (Jan 2000 - Dec 2009) of unemployment rate and suppose we would like to use these in order understand the unknown underlying stochastic process that ...
- 4,792
8
votes
1
answer
324
views
Detecting changes in large number of time-series that share seasonality
I have large number of time-series that are independent of each other, but share some seasonality patterns. I need to detect anomalies/changes (increased volume, change in mean), that appear in the ...
- 141k
8
votes
0
answers
4k
views
Which loss function to use when training LSTM for time series?
I'm experimenting with LSTM for time series prediction. The example I'm starting with uses mean squared error for training the network.
I know that other time series forecasting tools use more "...
- 12.1k
8
votes
0
answers
1k
views
How to choose the best time window using structural times series with loess
My question is about the Cleveland et al. 1990 paper STL: A Seasonal-Trend Decomposition Procedure Based on Loess. The full citation is:
Cleveland, RB, Cleveland, WS, McRae, JE, and Terpenning, I. ...
- 81
8
votes
0
answers
2k
views
What is the intuition for testing seasonal difference with OCSB test and its correct application?
I have daily time series data of a shop's revenue. Now I would like to test for seasonal differencing with the OCSB test originally intrduced in (Osborn et al. (1988): Seasonality and the Order of ...
- 111
8
votes
0
answers
923
views
Cross-validation in multi-level model
Suppose I want to estimate the out-of-sample prediction error of a boosted regression model that has random intercepts and slops. There are $G$ groups and $N$ observations. If I want to estimate the ...
- 3,925
8
votes
0
answers
1k
views
Gauss-Newton method for MA parameter estimation
Please check my solution below for estimating Moving Average parameter using the Gauss-Newton (Linearization) method. I consider MA(1).
MA(1) model:
$$z_t=a_t-\theta_1a_{t-1}.$$
Solution:
The ...
8
votes
0
answers
1k
views
Measure score change over time while accounting for baseline differences
I'd like to test for and estimate group differences in NIHSS (National Institute of Health Stroke Scale) change between hospital discharge and three months after hospital discharge.
Because the score ...
- 3,814
8
votes
1
answer
2k
views
How run Random Forest when there is temporal structure in the data
I am used to data sets that dont have a time component. In
In reading up on time series data i learned the importance of transforming the data into stationary data before applying the ARIMA model to ...
- 81
8
votes
1
answer
5k
views
Can one force an ARIMA forecast to be positive?
I have a an ARIMA model which gives a pretty good forecast when compared to actuals. However it occasionally dips to negative values, while the quantity being predicted can never be negative.
Is ...
- 12.1k
8
votes
1
answer
802
views
How to subset alternatives in nested multinomial logistic regression?
I am trying to predict whether or not captains in a particular groundfish fishery choose to fish on any given day and what variables may influence that decision. Originally I had planned on using ...
- 81
7
votes
0
answers
74
views
+50
Why don't we typically worry about stationarity in panel data models with fixed effects?
Why don't we typically worry about stationarity in panel data models with fixed effects?
In time series analysis, stationarity is often a crucial assumption. However, I've noticed that in applied ...
- 229
7
votes
0
answers
675
views
Does backcasting work the same way as forecasting?
Context: I have $K$ timeseries over the interval $[0,T]$ and $N$ timeseries over the interval $[S,T]$, and would like to backcast the $N$ timeseries over the interval $[0,S]$.
I am quite new to this ...
- 221
7
votes
0
answers
340
views
Features for binary time-series event prediction
This question is somewhat inspired by the answer to Features for time series classification.
The difference to that question is that I have a dataset with multi-dimensional time-series where I have ...
- 541
7
votes
0
answers
887
views
Intuition behind MA(q) (moving average) time series forecasting model (i.e. 'MA' part of ARIMA) and implementation
The $AR(n)$ part of ARIMA makes sense to me. If
$$x_{t+1}=\sum_{i=0}^n a_ix_{t-i}$$
then we are making the intuitive assumption that the next time step will somehow depend on the previous time ...
- 227
7
votes
2
answers
2k
views
Standard Error of the cumulative value for time series
I have two time series, as in the picture below. The data was gathered experimentally. A practical example could be a measured mass flow rate, where I measure the mass flow rate over a certain time ...
- 121
7
votes
0
answers
872
views
Time series models (e.g. ARMA) a type or extension of GLM? Particular/stipulated forms of dependence in time series models
I am trying to understand the relationship between ARMA Time Series models and the GLM (Generalized Linear Model) family of models. As far I know, all GLMs have the following 3 components: 1) random ...
- 4,792
7
votes
0
answers
1k
views
Time Series forecasting with Gaussian Processes
I am trying to forecast various time-series with Gaussian Processes, using the functional approach like in the Mauna Loa example in section 5.4.3 of "Gaussian Processes for Machine Learning". (X = ...
- 91
7
votes
0
answers
3k
views
Edge detection in time series
I have a time series (data here) which contains several square-wave jumps, as well as some physical signals of interest. An example is shown in the top panel of the figure below. There are square wave ...
- 301
7
votes
0
answers
734
views
Multitask Gaussian Process on multiple multivariate time series
I am in the process of working with multitask gaussian processes (the ones introduced by Bonilla et al in this paper).
I am now interested in applying the MGP to multiple multivariate time series.
...
- 181
7
votes
1
answer
666
views
Expectation Maximization intuitive explanation
Given a set of events {A, B, C, D, E} that occur once each month for n years:
...
- 161
7
votes
0
answers
3k
views
Interpreting Negative Binomial Time-Series
I'm working with time-series data for someone else that counts events related to emergency departments over a 48-month period during which closures occurred and would like to investigate the effect of ...
- 276
7
votes
0
answers
2k
views
Irregular Seasonality in time series
I understand seasonality of a time series normally means a cyclic component with constant frequency. For example, the frequency is 24 for daily cyclic trend of hourly data. One of the basic models ...
- 567
7
votes
0
answers
601
views
Empirical distribution function of overlapping time series data
If we model asset return volatility for periods of more than one (say more than one day) there is the square-root rule which holds true under some assumptions.
On the other hand practitioners ...
- 3,516
7
votes
0
answers
1k
views
Identification of peer/neighborhood effects in a multilevel framework
My question concerns estimation of “peer effects“ or “neighborhood effects” in a multilevel framework. The idea of such an effect is that the behavior of a household (on level-1) is influenced by the ...
- 175
7
votes
0
answers
933
views
Where can I find resources to learn about change-point analysis ?
Where can I find resources to learn about change-point analysis ?
Hopefully, someone can advise me a textbook to read and it will cover both univariate change-point analysis and multivariate change-...
7
votes
0
answers
4k
views
Time series clustering: Fourier transform and PCA
I have biological time series (9 years long) of the biomass of species which logically exhibit a seasonal pattern. I would like to cluster them into a few groups based on their typical seasonal ...
- 341
7
votes
0
answers
133
views
How to form a confidence band around the trend fitted from time series data
I have a time series data set. I can decompose it and get the trend but I would like to put confidence ranges around the trend (past) not the forecast-ed component. The decompose function also doesn'...
- 171
7
votes
0
answers
1k
views
Stationarity tests for time series
I am currently working on time series modeling, especially on stationarity tests. For this purpose, I am extensively using Pfaff's book "Analysis of integrated and cointegrated time series with R" and ...
- 303
7
votes
0
answers
385
views
Forecasting a complex time series by splitting into subseries
I have finance data that I need to forecast out for 7 years. My data is generally debits and credits, and those are split into a number of sub-series which share common traits (e.g. similar ...
- 1,561
7
votes
0
answers
214
views
Time series modeling the number of users of a mobile app
I want to model the number of users of an mobile app. This app has two kinds of users: free and paid. I thought of this autoregressive model:
$x_t = Ax_{t-1}$
with $x_t$ being a 4-dimensional vector,...
- 2,072
7
votes
0
answers
867
views
Classification of multiple time series and case level attributes
I'm pretty new to machine learning so wondering whether someone can help check my thinking or point me in the right direction!
I need to create a classifier which can predict an outcome for a person ...
- 925
7
votes
0
answers
374
views
How to denoise a "Poissonous" time series
I have $N$ time series each of which can be modeled as $$y_{kt}=Ax_{kt}+b+\varepsilon_{kt}\quad(1\le k\le N,1\le t\le T),$$ where $x_{kt}\sim\text{Pois}(\lambda\Delta t)$ and $\varepsilon_{kt}\sim N(0,...
- 1,796