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

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Steps to perform time series analysis

I'm trying estimate an autoregressive model with an exogenous variable. It's about the impact of changes in oil prices on the economy. I'm planning on regressing gdp growth rate on its own lagged ...
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ARIMA, adjustments and intervention analysis

I have very little knowledge of time-series analysis (despite my stat master - didn't do anything else than an introductory course) but now I'm facing a statistical problem whose answer is this very ...
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Time delay embedding

TIme delay embedding theorum given by Takens says that from a time series of a single variable $x(t)$ we can contruct multiple vectors and is given by the formula $X(t_i) = [x(t_i),x(t_i+ ...
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Is the Durbin-Watson test appropriate for count data

In determining if there is any serial correlation in a time series of count data, is the Durbin-Watson statistic or similar approaches appropriate? I ask this question because the dwtest implemented ...
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joint p.d.f. of stationary time series variables

if a stationary time series verifies that each variable depends only on the variable before it, and the joint p.d.f. of xi and xi-1 is f(xi-1,xi), which is the joint p.d.f. of xi,xi+1,xi+2, and of ...
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stationary time series example

Please, could anyone give an example of a stationary time series? I mean, if for instance $x_{1}$, $x_{2}$, $x_{3}$, $x_{4}$, $x_{5}$ are the 5 first random variables of the series, what would be the ...
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Statistical thresholds for large time-series

I am working with matrices, of the size N*M, where each cell corresponds to the Pearson's correlation between two time series. I want to threshold each matrix such that it would retain only ...
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Forecasting ar(p) for several counties

I have a data set of prices, these prices vary across time and across area. I have 18 areas with 32 time periods. What i want to do is forecast these prices, i have found that a AR(3) process fits ...
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51 views

Variance of $\bar x$, simulation with non-iid observations

So I know that the variance of $\bar x$ is usually computed as $\frac{\sigma_x^2}{n}$, and that this assumes the observations are independent. If instead, the observations have some positive serial ...
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Time in years(x axis) shows years in .5 interval - Time Series using R [migrated]

dsales1156ts<-ts(dsales1156,frequency=365,start=c(2011,6)) # This is my time series created with daily sales data when the first sale day is 6th January 2011. plot(dsales1156ts) The ...
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Obtaining the Psi Weights of a seasonal ARIMA in R

I am trying to quantify the effect of a future random shocks on my seasonal ARIMA model. If I have understood the theory correctly, the easiest way is to express my seasonal ARIMA model in its "random ...
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What is the best test to estimate the correlation between binomial/categorical dataset?

I'm trying to analyze if there are correlations between binomial dataset. I have binomial data (presence/absence) of two variable in different periods and I need to know what is the best way to find ...
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51 views

Standard techniques for forecasting revenue growth of a company?

I was curious what sort of time series models were the standard for doing this type of analysis. I have weekly sales data for the company - I could cook up my own time series model but would like to ...
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training period selection forecast (error analysis)

I have been lately testing the best training period length to perform a forecast. I have tested it for various days of training period length, among them 60 days and 30 days. My methodology is quite ...
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Statistical Methods for Calculating Vending Machine Refill

Am looking into statics to help support a project I am undertaking. The project scope concerns intelligent replenishment / refill of vending machines. During an onsite service, a technician must ...
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How to normalize time series?

This is a general question on normalization of data so that all the variables are within the same range. Why do we normalize data in pattern classification? How to normalize time series which is ...
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37 views

Is PCA appropriate for comparing subsets of panel data?

I have a large panel (5000+ subjects, 4 variables over 182 periods), and I've identified particular Granger-causal relationship in a large subset of those subjects (30% or so). I would like to somehow ...
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R function which uses innovations algorithm?

I can't seem to find much info on the following: I have a dataset D at time t which I use to fit an ARIMA model. I forecast the value of the time series at time t + 1. Now, when I'm in t + 1, I would ...
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Determine the threshold value and number of regimes with delayed variable

I am currently working on a threshold model for the exchange rate between UK and US. I have not got background knowledge on this model so I am really stuck on how to determine the Threshold value, ...
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Using AIC to determine best ARIMA Model

I'm trying to fit an ARIMA model to housing data set. Playing around with the p's and q I was able to get an ARIMA Model (2,1,2,)(2,0,0) with an AIC value of AIC=4946.76 I used auto.arima to see if I ...
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Approaches to Subsampling to exceed a Population Mean

I've studied a large amount of Probability & Statistics, but I'm embarrassed to say I've forgotten too much of it. Would appreciate any pointers anyone has about this: I have a set of about 360 ...
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Intervention Analysis - Pulse over several periods

I have a couple weekly time series and an intervention occurred over several weeks and then for some, after a period of no intervention, began again. So, the pattern is off for a period of weeks, then ...
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Getting Residuals to be White Noise

I'm on a time series project for an undergraduate course. For the project I'm trying to come up with an ARIMA model for the housing starts data set. ...
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How do I estimate a time series regression using GMM in the way proposed by Acosta-Ormaechea and Morozumi (2013)?

In their paper Acosta-Ormaechea and Morozumi (2013) propose a use of GMM for estimating a regression in which they try to find the impact of reallocating public expenditure from some unproductive to ...
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Dynamic Time Warping for irregular time series

I have been reading a lot about Dynamic Time Warping (DTW) lately. I am very surprised that there is no literature at all on the application of DTW to irregular time series, or at least I could not ...
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Problem when doing pre-whitening before ccf analysis [migrated]

I have following R code which does not work when trying to pre-whiten other series by the model generated for the other series. ...
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Estimation of regression with autocorrelated errors

In a book it is written that, In regression work we typically assume that the observational errors are pairwise uncorrelated. But in most time series data , the successive residuals have tendency to ...
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Checking for seasonality in data set

I'm working on housing data set for a project for a undergraduate time series course. I'm trying to see if there is seasonality in the data. I used the following commands but do not know how to read ...
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is it possible a nonstationary time series, to produce a stationary ARMA model?

I Have a variable (time series) which is nonstationary. I found that from the graph which seems to have a stochastic trend and the correlogram has a typical nonstationary pattern. After that, I've ...
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How do I investigate how long it takes one variable to affect another in a time series

I am a total newbie when it comes to time series, so it is quite possible this question is duplicated somewhere else, only that I cannot find it because I don't know what this feature is called. My ...
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1answer
28 views

Filtering using a SARIMA model in R

I am not an expert in statistics, but I would like to work on a SARIMAX model representing power consumption. The exogeneous variable would be the temperature, but for now I found here I might need to ...
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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 ...
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ACF and PACF plot analysis

I am new to ARIMA, and I am trying to understand these lag plots. Are the following ACF and PACF suggesting that the lag of my time series is 4? If I am wrong, please help me understand these plots. ...
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Arima model - multi step forecast

The following code shows a forecast of the next 24 hours of my electricity prices with two exogenous variables. My problem is, that I don't know how to build a forecast for the next 3 days or more ...
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Holt Winters Initialization Issue

I am using an additive seasonal Holt-Winters model to compute confidence band of my data. I followed the HW initialization process described by Rob J Hyn­d­man. The confidence band is derived by ...
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Capturing Seasonality in Multiple Regression for daily data

I have a daily sales data for a product which is highly seasonal. I want to capture the seasonality in the regression model. How I can do it? I have read that if you have quarterly or monthly data, in ...
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Two or more time series. What is the best way to test whether one of them is leading and by what time period?

I am trying to prove/disprove that one time series is leading trend for the other ones. Two time series are (probably) independent and the movements are caused by some (let's assume unknown) common ...
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References suggested for multivariate analysis of several similar time series

I have a time series dataset that reports the hourly page views and social media shares of online news stories. What I hope to obtain is the relationship between the two variables. I would imagine ...
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Searching for time series inside another time series

I have a time series "A" and another one "B". I would like to find occurrences of "B" inside "A". Typically, "A" is much bigger (magnitude: millions of points) than "B" (magnitude: hundreds of points) ...
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Is this the proper way to create a simple linear time series model in R

I'm trying to create a simple ols model over time from a time series. Here's what I have cagr.lm.time <- lm(cagr.xts ~ time(cagr.xts)) Where cagr.xts is the ...
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Evaluating the temporal corelation model for which the differencing is performed for trend removing

According to the literature, for temporal correlation modeling the trend should be removed from the time-series data. We choose differencing for removing the trend. I would like to know: When we ...
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Quantifying Change in a Histogram Valued Timeseries

I'm attempting to do binary classification where my raw features are collections of histograms that are recorded in a time series. These histograms are scaled to sum to 1. To be more precise and ...
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Standard deviation of multiple root mean square values

I am calculating the R.M.S. of a periodic signal that has a stochastic component to it. For every period I am able to calculate a value for R.M.S. using the following function: $R.M.S. = ...
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If two time series $X$ and $Z$ follow $0 \leq Z \leq X$, can we say that $\text{var}(Z) \leq \text{var}(X)$?

Now I see it can't hold. Thank you for the counter examples... You guys rule! Thank you very much for your comments! I added, however, some observations that were missing. Most importantly is the ...
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R: Fitting a model with periodic, nonlinear and categorical components

Can anyone give me some advice on how to fit a model with linear (some categorical), non-linear and time series components in R? I don't want to use a non-parametric model like a Loess smooth or ...
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Mann-Kendall test STATA

I am new in this forum. I am beginning to work with time series, I have a daily (25,000+ observations) temperature dataset (01/01/1946 - 07/01/2014) I want to test for the following: Trends: So ...
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Forecasting time series with missing data and irregular intervals

I have a data set of medical drug stock levels at health centres and I want to forecast monthly consumption over the following 3-6 months. However about 30%-40% of the data is missing and some of the ...
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MAPE is high for daily sale prediction

I have daily sales data from 2011 to 2013. I have to do prediction for 2014.I have used arima and exponential method to predict the daily sale, but it is not giving the better result. MAPE is around ...
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Getting expected value of future value with time varying data (credit card revolving and fee data) . Customer lifetime value

I have a credit card data and that contains monthly amount of revolving and amount of fee for each customer. As a bank perspective, I want to get the expected value of future revolving amount and fee ...
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Calculate the average of hourly data of three sensors

I am trying to calculate the average of hourly data of three sensors but the hourly timestamps of all three sensors are different. How is it possible to measure the average of hourly data of all three ...