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

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ordinal logistic regression with time series

At several time points, I sample different individuals from a population (say 60 ind/time point). I assign to each of them one category (either low < middle < high). I then want to model this ...
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10 views

Calculating the probability of an event being early or late by a certain amount

I'm very new to this so I assume there's a better way to ask this that I just don't know about. Please point me in the right direction. Suppose I'm running a library and books are due 30 days after ...
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8 views

Predict function in r - time series

I have built a model on my training dataset where I have used the difference of the log(Y) i.e. diff(log(Y)) as my response variable. Now if I want to use this for predicting the Y variable in my ...
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33 views

Offline Hidden Markov Model for time series analysis

One of the main principles of HMM is that the future state is dependent on previous state. This method is widely used for time series segmentation. However, for offline segmentation one can run HMM ...
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13 views

Seasonal Time Series Modeling in SPSS Modeler (or R) & ARIMA

I've got a question regarding ARIMA modeling. I am having a hard time to make to model out the seasonalities of my time series. The pictures below shows my tries in modeling. The topic is to ...
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1answer
25 views

What cross validation use with time series database?

I'm using a regression tree to predict/forecast a daily bases data. I'm wondering to use a cross validation to train and predict all the data. What cross validation procedure I may use?
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109 views

Covariance of two time series driven by a restricted VAR(1) model

Suppose that I have two time series $X_n$ and $Y_n$ where: $$ X_n = \rho_x X_{n-1} + \epsilon_n $$ and $$ Y_n = \rho_y Y_{n-1} + \rho_{xy}X_n +z_n $$ Here, $z_n,\epsilon_n$ are independent random ...
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White's Reality Check Hypothesis Testing question

I am running a hypothesis test based on White's reality check p-value. I am getting a weird result for my univariate time series of returns. In essence, I am following a code on MATlab to run the test ...
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3answers
54 views

Forecast accuracy metric that involves prediction intervals

I'm in the process of generating a time series forecast for a company's product revenue and am looking for some way to show accuracy over time - e.g. after say 6 months they want to see how the actual ...
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260 views

Why does multiplication in the frequency domain equal convolution in the time domain?

This question came in the context of understanding how to get a distribution of a sum of two iid random variables. I'm working through the top answer to this question Consider the sum of $n$ uniform ...
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1answer
47 views

Model selection for and forecasting of arctic oscillation (a seasonal time series)

I'm having a doubt with a time series. I have to find the best model for it and use it to do some forecast. The data are about the arctic oscillation (AO) from 1950 to 2015. The series is clearly ...
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10 views

Forecast function for a MA(2) time series

For the quadratic loss function and the lead time $l = 1$, derive the forecast $z_t(l)$ for the MA(2) time series, and then calculate the prediction error $z_{t+l} − z_t(l)$ and its first and ...
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Relationship between observations [on hold]

My question is how can test the hypothesis H0 H0 : Relationship between observations. How to get this? Thanks.
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10 views

Dynamic regression models in SAS

Could anyone please advise on any relevant material on utilising dynamic regression models in SAS? The response variable is a continuous variable with multiple drivers with various types (categorical, ...
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16 views

Individual versus group-wise significance in ARDL context

In an ARDL model approach, what is one supposed to do if the F-bound test shows insignificance while some variables have significant long run and short run coefficients (the error correction term is ...
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1answer
21 views

What does the error “pre.period must span at least 3 time points” in the CausalImpact R package mean?

I've been encountering the error "pre.period must span at least 3 time points" when using the package. Can someone help me understand why the package requires me to have at least 3 time points and ...
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21 views

Time Series Regression With Splines

I have time series data for 63 years on 3 variables (V1, V2 and V3). I need to fit the most appropriate time model to all three variables individually (linear trend, quadratic trend, cubic trend and ...
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18 views

Rolling estimate of Andrews.Chen, median unbiased estimator of an AR model in R? [migrated]

Given an AR(4) process, this can be rewritten in an ADF form as In order to estimate $\rho$, that in this case represents the sum of the autoregressive parameters, there is a procedure proposed by ...
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1answer
38 views

Modelling stationary and integrated time series in one system

I am currently investigating commodities and their impact on the oil price. I have 8 variables of different stationarities $y$ = dependent variable (oil price) is non-stationary I(1); three ...
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1answer
30 views

Handling missing values in function `prewhiten` in R [on hold]

I am using function prewhiten from "TSA" package in R. I get an error about NA values, but I don't understand it, because I ...
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1answer
28 views

Can negative relationship between X and Y be spurious

Let's assume I have X and Y and both X and Y have positive relationship. In such case in which both series trend in the same direction, we need to test for cointegration to be sure that relationship ...
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22 views

Print significant auto-correlation values in R [on hold]

If I do an autocorrelation test in R (using function acf), I get a great graph, and the horizontal lines show the cutoff of significance. Function ...
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1answer
35 views

Estimating accurately the mean of an autocorrelated bounded integer time series

I have a bounded integer time series $X_{1:\infty}$ ($1\leq X_k\leq M$), and I want to estimate the mean $$ s = \lim_{L\to\infty} \frac{1}{L}\sum_{k=1}^L X_k. $$ I'm assuming it exists and that $X_k$ ...
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LSSVM Prediction using LS-SVMLab toolbox v1.8

I'm trying to forecast a time series of air passengers using LSSVM with the help of the LS-SVMLab toolbox v1.8 from http://www.esat.kuleuven.be/sista/lssvmlab/, specifically the NARX model function. ...
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22 views

Similarities between Pade approximations and ARMA(p,q) in time series [on hold]

I am wondering if someone would mind explaining how Pade approximations in calculus and ARMA(p,q) in time series are similar.
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19 views

Modelling Reflexivity with a Time Series Model

I'm new to this forum so hi to everyone! I would like to find a time series model that is capable of modelling two time series with following properties: 1) consider time series X and Y. X is ...
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22 views

Time series regression coefficient interpretation with differenced independent variable

I'm working on a project on time series regression. The independent variable was non stationary so I took first differences to stationarize it. Now when I regress it against the dependent variable the ...
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18 views

time-series analysis / forecast compared to real planning (controlling) departments?

The following case study: Planning and forecasting in a volatile setting by Amy Wheeler, Nina Weitkamp, Patrick Berlekamp, Johannes Brauer, Andreas Faatz and Hans-Ulrich Holst, published in Rob ...
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36 views

How to forecast daily sales of multiple items?

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38 views

Vector autoregression: many variables (10), short sample (100)

Suppose there are ten observation sites along the road. A, B, C, D, E, F, G, H, I, J. We obtain data at each site once in a day, in this order. That is, first go to the site A at 9:00a.m., and when ...
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34 views

Dynamical time warping for dynamical system classification

I am trying to find a way to classify and segment a large set of time series that each individually describe a dynamical system. I wanted to know if the following idea for doing so is a feasible ...
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21 views

X-13 Seasonal Adjustment trading day peak in regarima residual

I have a time series of air passengers with a total of 143 monthly observations. Having done the seasonal adjustment using X-13-ARIMA-SEATS, the final diagnostics seem to suggest that the seasonal ...
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20 views

Measuring promotion baseline smoothing

I am trying to estimate promo effectiveness for a retail product. To do this I have taken monthly quantity sold data for the product. My question is - Is it logical to model the sales baseline (sales ...
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Jittery sampling of jittery events - what to prioritize

Stat beginner with a practical problem. In an industrial application, I'm sampling a repetitive production process, to generate a histogram of the cycle time. The cycle time of course varies somewhat ...
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17 views

Time series pattern match in large datasets

I want to find in a time series pattern that look similar to a predifined one. At the moment I am using DTW to do this and it works well. But now I want to use it for larger datasets like finding a ...
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25 views

A time series which cannot be made stationary

I have a series of data points, of days between successive earthquakes. I am trying to model time between earthquakes, in order to predict waiting times. Using Minitab I have tried differencing, ...
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1answer
34 views

VAR/VECM/ARDL optimal lag selection

Question 1: Is it necessary to consider AIC and the BIC criteria when selecting the lag for a VAR, VECM or ARDL model OR can I use something else? Example: Can I pick 12 lags because the model simply ...
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References for Combine Seasonal Period in Seasonal Time Series

I'm in a middle writing my thesis. I got confused for find the references which said period of seasonal could be chosen from the small period which already containing another periods. Here is my ...
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76 views

Time series forecasting with many predictors

Suppose I want to forecast a time series $y$ using its own lags and a large set of potential candidate predictors $X$. The model could be specified as follows: $$y_t = a + \rho \cdot y_{t-1} + \beta ...
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Google Trends: Stitching 90 day periods of daily data together

Google Trends lets you see the amount of researches made on for a term on google during a set period of time, normalized between 0 and 100 (depending on the highest value in that period). I would like ...
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Rules of integrated series and balanced regressions

Background There are various rules of linear combinations of integrated series. Let's just consider the $I(0)$ and $I(1)$ cases. For example, if $x_{t} \sim I(1)$, $y_{t} \sim I(0)$, then $ax_{t} + ...
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26 views

PCA to decorrelate and classify timeseries

I have a labeled dataset where each subject belongs to one of two classes A or B, with A ...
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1answer
19 views

Interpretation of level, trend and seasonal indices in Holt-Winters exponential smoothing

I am trying to learn Holt-Winters exponential smoothing. In the algorithm there are three indices involved (level, trend, ...
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1answer
23 views

Algorithm to determine a point in time series data, after which probability of increase in value is very low

I am working with dataset which contains number of movie tickets sold per day. This is basically a count of total number of tickets sold, for a particular movie, for each day after its release date. I ...
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43 views

Predicting time series using `arima` or `fitlm` in Matlab?

I have 6 sequences (time series); they all belong to the same variable. I divide each sequence in two parts having 80% and leaving the last 20% for validation. I am doing the analysis and modelling in ...
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R: How to pack the seasonal components of time-series to an array-like structure? [closed]

I am trying to calculate the decomposition of 34 time-series to study the similarity of seasonal components, as explains this this tutorial I start from a CSV that, after some transformations and ...
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26 views

Testing for white noise for non stationary time series

I could not find a question where testing for white noise for non strick stationary non parametric time series is adressed. Per definition white noise is stationary. But finding a series that is ...
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2answers
26 views

Modelling effect of advertisement on sales with ARMAX

I am trying to model the effect of advertisement on sales in Stata. The data is weekly and there are around 150 observations. I started by applying an ARMAX(1,0,1) model with the following exogenous ...
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Suggestions for Neural Network Structure for Time-Series prediction with constant covariates

I've been working on a time series prediction problem and wondered if someone has run across a similar problem structure & can make a suggestion on how to structure the training data, network, or ...
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Time series and images : difference and terminology

A time series is an ordered collection of random variables. Considering a one-dimensional time series $A_i = {a_{i1},a_{i2},\ldots,a_{it}}$ where $t$ denotes the time index. So, the time series is a ...