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

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2
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18 views

Zero p-value for Dickey-Fuller test

I feed sample time series data in statsmodels Dickey-Fuller test, and as a result I get exact 0 (zero) for p-value. I'm not sure how to intrepret that. I think that it doesn't imply stationarity as ...
0
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21 views

Project using arima [on hold]

Year Ageing population 1974 32239 1975 33111 1976 34343 1977 35096 1978 35977 1979 37918 1980 39473 1981 40366 1982 42201 1983 43488 1984 44232 1985 45206 1986 ...
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8 views

Estimation of VECM via ML and OLS

Take a vector error correction (VECM) model: $$\;\;\;\Delta y_t=\Pi y_{t-1}+\Gamma_1\Delta y_{t-1}+...+\Gamma_{p-1}\Delta y_{t-(p-1)}+\varepsilon_t$$ where $\Pi=\alpha \beta'$ and each row of ...
1
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0answers
26 views

Skewed posterior distribution on constrained parameter space for Bayesian inference of MCMC. Advice on what to do?

I am running a fully Bayesian MCMC procedure to estimate some time series models, and my model has a lot of parameter estimates. In particular, one of these parameters, $\phi$, is $\in [-1,1]$. The ...
0
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2answers
33 views

Multivariate Gaussian distribution - covariance and variance

In my previous question Density function for AR model, the density function of AR model has the covariance-variance matrix given as $\sigma^2 *V_p$. In multivariate gaussian distribution, the pdf ...
0
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0answers
7 views

Two ways to model pre/post/treatment setting. Which one is preferred and why?

I have 20 individuals randomly distributed into two groups(treatment vs non-treatment) and test_score was measured before/after the treatment. My central goal is to measure the effect of the ...
1
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0answers
10 views

Estimation of parameter depending on other paremeters

PROBLEM STATEMENT: Let $X$ be random variable in $m$ dimensional space. The distance between each pair of vectors $x_i^m,x_j^m$ is $D_{i,j}^m =d(x_i^m,x_j^m)$. There is a measure - Correlation Sum, ...
0
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0answers
15 views

Holdout MAPEs in SAS PROC ARIMA and SAS Forecast Studio don't match

I have a Time Series (ARIMA) model in SAS, modelled using proc ARIMA which I am trying to replicate in SAS Forecasting Studio. What I see is that The parameter estimates in both are very similar ...
0
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1answer
18 views

Time series tracking queue optimization problem

In order to track prices of many different products from different sources, I must optimally schedule a group of trackers dedicated to price collection (ie. collect one price at a time for each ...
-1
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0answers
19 views

Matlab: Time lagged time series and its correlation

Let, $x_t = [y_t,y_{t-1},...,y_{t-p}]'$ where $y_t$ is the output of an autoregressive process of order p=2 excited by Gaussian white noise of zero mean. $y_t$ is a vector output of the AR process: ...
0
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0answers
12 views

AR(n) model with exponents

When we discuss a (time-series) model $AR(n) = \Sigma_{i=0}^n Y_{t-i} + \cdots + \epsilon_t$, we use $n$ to refer to the number of time steps back the autoregression includes. In other such models, we ...
1
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0answers
8 views

A question about Dickey-Fuller Unit-Root Test

I am reading Dickey-Fuller Unit-Root Test in Time Series Analysis with Applications in R by Cryer and Chan and have trouble understanding some discussion on equation (6.4.1). So they took this ...
1
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1answer
31 views

How to predict the time series data

I have no background of advanced stats. I am an engineer and I have the following data. I am representing it as a decent graph for better understanding. I want to forecast the collision for the next ...
1
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0answers
24 views

Time Series Decomposition - autocorrelation of error term

I would like to do time series decomposition, but the error term has a serial autocorrelation at the end and I am freaking out because I have really no idea what to do with that. How I did it? I ...
0
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0answers
16 views

Comparing influence of single independent variable on two dependent variables (time series)

Scenario description: Temperature has been measured at $k+2$ different depths in a borehole. Measurements of the temperature were taken once each hour over a period of about 3 months. So observed data ...
1
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1answer
37 views

Is time-series an appropriate method to model data sampled at widely irregular time intervals ?

I am relatively inexperienced with data analysis. My question: Is time-series an appropriate method to fit trends to data sampled at widely-irregular time intervals such as forests of different ages ...
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0answers
13 views

Augmented Dickey Fuller says it's okay. But is it really?

This is original sales data. The biggest spike occurs during winter holidays with a consequent customer inactivity around early January. My Augmented Dickey Fuller Unit Root test is quite significant ...
4
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3answers
103 views

How to interpret PCA on time-series data?

I am trying to understand the use of PCA in a recent journal article titled "Mapping brain activity at scale with cluster computing" Freeman et al., 2014 (free pdf available on the lab website). They ...
0
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0answers
58 views

Can I use the correlation value to measure the correlation between two variables when observations in each variable have auto-correlation?

I have two variables: urban areas protected areas. My observations are urban areas and protected areas in each year. But these observations are the cumulative ones, so observations in each ...
0
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0answers
18 views

One Step Ahead Forecasts Using Predict() in R

I just fit a model to a time series. I am now required to generate a 10-year extrapolation forecast of my model. My model includes a time term, a time^2 term, 12 seasonal dummies, and 4 lagged ...
2
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0answers
27 views

Using quadratic programming to fit a piecewise linear model plus seasonality

I am reading this paper on fitting an L1TF model to data using quadratic programming. Section 7.4 states how one could add seasonality to the model however it doesn't go very far into it. I am trying ...
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0answers
20 views

Selecting an appropriate VAR model

I would like to receive critical comments on an idea explained below. Suppose I have variables $x_1$ through $x_K$, and this is a time series setting. My aim is to forecast variable $x_1$. I know ...
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0answers
42 views

Stationarity ⇒ homoscedasticity? [duplicate]

If my data is stationary, can I also write that it is homoscedastic? Does stationarity imply homoscedasticity of the data?
0
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0answers
14 views

Basic questions about stochastic gradient descent / Robbins and Monro algorithm

I have a LOT of time series observations and I would like to estimate a simple AR(1) model $$ y_t =c+ \phi y_{t-1}+ \varepsilon_t \qquad \varepsilon_t \sim \text{N}(0, \sigma^{2}) $$ with parameters ...
7
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4answers
258 views

Analyzing up/down patterns in short time-series data

I have not worked very frequently with time-series data, so am looking for some pointers as to how best proceed with this particular question. Let's say I have the following data - graphed below: ...
2
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1answer
133 views

How to implement model in R?

i would like your help to implement this model in R or more explicity where yt = monthly mean values μi = mean value in month i, i = 1 . . . 12 . I1;t = Indicator series for month i of the ...
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0answers
18 views

How to compute the out-of-sample log-likelihood function?

I am doing some empirical work based on realized GARCH model, whose log likelihood function is given as $$ l(r,x;\theta) = -n\log(2\pi) -\sum_{t=1}^n \left[\log(h_t) + \frac{r_t^2}{h_t} + ...
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1answer
35 views

proving the asymptotic distribution of the mean

Let ${X_t} = \mu + \sum\limits_{j = - \infty }^{ + \infty } {{\psi _j}{\varepsilon _{t - j}}}$ with $\varepsilon$ is a white noise iid with variance $\sigma^2$ , $\sum\limits_{j = - \infty }^{ + ...
0
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0answers
22 views

what is the best method of smoothing time series for product share data?

I am having a share data for products presribed over period of time.The share is calculated like for 3 products each one 1/3 share and like that,where products may vary and hence their share. What is ...
0
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0answers
9 views

Obtain the graph of the autocorrelation function in ARIMA models [migrated]

I am implementing an ARIMA model in Python for forecasting U.S. GDP. I am interested in obtaining the graph for the autocorrelation function. I obtained the values for ACF but I can not see the ...
0
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22 views

Finding genuine arrears and default arrears from rent payment patterns

I am currently working on some housing data - in particular analyzing the tenants' rent payment information and I am stuck on progressing with the following: I have to classify tenants based on their ...
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10 views

Coarse-resolution subsampling of time-series data

Suppose I have time series data with a very fine resolution, e.g. 100 datapoints per second. I want to report this data to some service that can only take data at 1 point per second. I need to do ...
0
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0answers
22 views

Techniques for comparing two windows of data in a time series

I'm working on a small independent project in R, trying to make my own (very crude) forecasting method. The general idea of the component that is giving me trouble is trying to compare two windows of ...
1
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0answers
16 views

Training/ Test Data with Time Series Model — Forecast with Training Model, or with Model based on Full Data?

Okay, I have a couple books on time series forecasting, but perhaps I need to read a couple more. Here's my question. You want to be able to validate a forecasting model. So you split the data into ...
0
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1answer
29 views

R times series — correct use of forecast() and accuracy() in forecast package

Cross-posting this from Stack Overflow, because it's a bit of a stats/ technology cross-over. I'm relatively new to R and the forecast package I believe authored by Rob Hyndman. I'm having trouble ...
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1answer
55 views

Analysis of residuals

For my master thesis I have implemented following forecasting models: naive (just to check) decomposition method exponential smoothing (single/double/holt-winters) SARIMA Now I need to do the ...
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0answers
24 views

Using A Time Series To “Scale” Another

I know the "average theoretical cost per impression" for Jan 13 - Dec 13. I have other monthly time series for "total # of impressions", "total # of clicks" and "total number of conversions" for Jan ...
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8 views

Given two time series where one is dependant on another how do we find the spectrum or acvf in order to then find the spectrum.?

$X(t) = aX(t-1) + Z(t)$ and $Y(t) = bY(t-1) - X(t)$ how do you find the spectrum if $a=b <1$ and if $a\neq b \neq 0$ and $a,b <1$?
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27 views

Hold out sample vs. cross validation for time series, and how to perform in R

I think out-of-sample validation testing for accuracy is essential in initially judging what time-series forecasts to use. In any case, I've been doing some reading on the two most common methods, ...
0
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0answers
11 views

Optimal length of observation window

I have a sample of N stocks, with time-series of daily returns. For each stock, I would like to compute the sample univariate variance of returns, using a rolling window. These variance will be used ...
0
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0answers
26 views

autocorrelation in evaluating time series forecasts

I'm having some trouble wrapping my head around whether using Holt-Winters ETS or an ARIMA model for forecasting sales figures (which are highly seasonal). I'm been using R and the Forecast package ...
0
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0answers
14 views

VAR model for price forecasting in multiple time-series context. How to get “real figures” as forecasts?

Sorry for the rather long introduction, but since I was (legitimately) critizised for not explaining my cause and questions enough, I will do so now. I would like to conduct a (price)-forecast based ...
0
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0answers
11 views

handling trend in predictor and response variable

I am trying to create a linear regression model containing two predictors and 1 response variable. My response variable has a short term pattern, i.e. surge during weekdays and slump during weekends ...
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1answer
29 views

What does the Argument “type” in VAR() - function do?

Right now I am working with vector autoregressive models in order to make 3 months forecasts for a commodity good (sawlogs) y. I have several time-series of "follow-up-products" of sawlogs that should ...
0
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16 views

Predicting the near-future values using an unevenly sampled time-series data

Summary Need help with predicting the near-future values using an unevenly sampled time-series data. Data is collected as events, and is converted to time series. I have tried out a few approached ...
0
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0answers
16 views

creating an indexed dummy variable as a predictor in OLS

I am performing on OLS with two predictors and a response variable. The data is a time series of 450 days approximately. There is an irregular pattern in my response variable - it sometimes ...
0
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1answer
16 views

How do I deal with missing data for a repeated measure collected over time?

I have a real-life data set, with only one "measured" variable (i.e., patient waiting time). This single variable is collected weekly, in different clinics, for different providers, across time. I am ...
2
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2answers
65 views

Transfer function in forecasting models - interpretation

I am occupied with ARIMA modelling augmented with exogenous variables for promotional modelling purposes and i have hard time explaining it to business users. In some cases software packages end up ...
1
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1answer
34 views

Time series modelling

Here is my problem: I basically have 20 or so variables (I have 1000 of these values over an increasing time axis). I want to calculate the weights of these input variables. I am going to try Linear ...
0
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0answers
20 views

time series definition for time between failure

I recently started on forecasting time-between-failure for failure of different components in a truck. I saw a few papers which used ARIMA to do the forecasting for number of failures at specific time ...