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

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Dectect White noise ACF - PACF Eviews

I found PACF and ACF like the following table . But, how can I decide whether there exists white noise? And what is white noise? If there is white noise, how can i build my best fit model with ...
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7 views

Identify outliers with median-absolute-deviation for timeseries data

I am having trouble understanding this particular method of detecting outliers in a time series. Below is the problem: I have a region-of-interest containing 15 voxels. Each voxel contains values ...
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10 views

Time series model

I have been give a task for this: "Construct the best-fit time series model for the mean and variance process underlying the portfolio returns, use the best time series model to forecast the mean and ...
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15 views

Suggestions for Time Series Exercises

Do you know of a good set of exercises, preferably with solutions, that would help me learn Time Series by myself? I was searching for solutions to the exercises of Shumway and Stoffer's «Time Series ...
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1answer
19 views

Interpretation of VAR and causality

I have two time series(X1 and X2) each having 900 records. I wanted to establish relationship between them and put it in ...
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1answer
31 views

Residuals Interpretation:Time Series Data

I am trying to use multiple regression for a time series dataset. I have values corresponding to a variable measured by 24 hrs for 4 months. Since there was a pattern which repeated every 24 hours I ...
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38 views

Least squares regression of NPS

I would like to monitor customer satisfaction over time and would ideally like to use NPS for that. Specifically, I would like to see if there's an overall trend over time. Could I regress Net ...
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13 views

Time Series: testing significance

Assume we follow a single companies stock over several years. This stock has a steady upward trend over the years we are recording its value. Then in the current year it takes a hard downward trend in ...
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23 views

Dealing with seasonality in time series with no trend

I'm working with quarterly time series data. The series does not have a trend but fluctuates around more or less a constant mean. I observe weak seasonality such that the values are somewhat lower on ...
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17 views

Clustering and Pattern detection of Moving Object

I have a data of moving object generated in time Series. The data generated on equal time interval (1 observation per second). I have the following information about the object movement: feature ...
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56 views

Dealing with spikes in data

A company sells chocolates. Demand is recorded weekly. The future demand is estimated using the sales for every week in the previous 3 years. But the sales pattern is corrupted by promotions that have ...
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2answers
41 views

Does this ARMA-inspired time process have a name?

I have to estimate a price time series for my thesis. We found that the price is reasonably well described by a process on this form, which looks like an ARMA(p, q) process but has an extra term: ...
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32 views

Maximizing oldest possible average date of dataset [on hold]

Let's say we have a dataset by date and dollars: ...
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9 views

What are valid ways of analysing predictors for a response variable that changes with time?

I have a cohort of similar patients who are likely to get a certain disease over time. I am trying to find out how some continuous health markers (e.g. weight) at time 0 are related to their disease ...
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17 views

How do I choose the correct model for a regression? [migrated]

So the central question of my project is to what extent does a country's level of export contibution towards GDP (i.e. exports as a % of total GDP) affect its GDP growth. I'm comparing this ...
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1answer
27 views

Interpreting VECM result

X1 , X2 , X3 and X4 are time series which are stationary at level. I want to establish long term relation between them. I am planning to use it as forecasting model for my work. I want to create this ...
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1answer
27 views

Time series Analysis - VAR or VECM

I have 4 time series. One of them is stationary and rest of them are not. I need to find relation between them. I will use AIC to decide lag length. Should I use VAR or VECM to find relation between ...
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20 views

How to represent an ARIMA(p,d,q) with dlm package in R? [on hold]

I've been using DLM package for modeling my timeseries in state-space format, and then use Kalman Filter to get better 2 step-ahead forecasts. Even though I've read the vignette and parts of their ...
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1answer
32 views

Need to extrapolate missing monthly data from annual data; the monthly/seasonal index is reasonably well-known

I have a revenue dataset for various businesses. For about half of those businesses, monthly data is available. For the other half, only annual revenue data is present. I know the seasonality of the ...
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8 views

rsquared relation in regression with ma(1)

Hi: I'm fitting a time series regression model with ma(1) errors so $ z_t = \beta_0 + \beta_1 * x_{t-1} + \beta_{2} * y_{t-1} + \epsilon_{t} - \alpha * \epsilon_{t-1} $ Although there are ways to ...
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14 views

Contemporaneous regression in R (VAR model)

I have two time series and I want to check the relation between them. I am using a VAR(3) model. I would also like to include the contemporary variable, something like this: Can we do this ...
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1answer
14 views

Best way to fit an ARIMA model when the values of the variables don't change

I have a time series with various features that record sensor data. It can be the case that the values are recorded although they did not change compared to the previous observation. Hence, the series ...
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1answer
21 views

Modelling turnovers by a random walk. Is it right?

I need to analyse a bunch of weekly time series that reflect the turnovers of various companies. I already read that return rates or share prices show stochastic patterns that can be modelled by a ...
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11 views

resources for temporal count data

I am currently in the middle of analyzing count data. The count data is gathered once per day for more than 100 days. There are two populations that are counted everyday. Is this type of count data ...
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10 views

How to tell in Panel Data how the independent variables affect the dependent for specific countries instead of the whole sample in total?

I am working on my Master Thesis on how some specific independent variables (like inflation) can affect a specific index which is the dependent variable. I am taking 25 annual values for 12 countries. ...
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32 views

Does a stationary process necessarily have to be mean-reverting?

I wonder about if a stationary process is by definition mean-reverting too. I know the formal definition of a stationary process, but I'm not sure about the definition of a mean-reverting process. ...
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26 views

forecast(method ='arima') ; auto.arima() function, how to avoid forecast not in line with history?

I am working on an alogorithm in R to automatize a monthly forecast calculation. I am using, among others, the forecast(method='arima') function from the forecast package to calculate forecast. It is ...
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19 views

Comparison of time series nested in Repeated Measures Design

The title might not do justice to the question I want to ask, but it's the best I came up with. Background In my experimental design study 30 users were asked to watch 8 videos (repeated measures ...
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1answer
28 views

The results and specifics from the 'qs' function in R

I'm not entirely familiar with the results of the qs() function in R's package seasonal. But, by what I understand roughly, the ...
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56 views

How are outliers dealt with in R after detected? [closed]

Once outliers in time series are detected in R how exactly are they dealt with before forecasting? I dont want commands to use i would like the method. Please do not give any answers to do with ...
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47 views

Residuals - What are they? How can i obtain them?

So, i have a data set. I decide to fit an AR(1) model to it thus obtaining a model of the form $X_t - \hat{\phi} X_{t-1} = Z_t \quad Z_t$ is $WN(0,\hat{\sigma^2})$ Which in matlab is given by ...
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27 views

Hidden markov model multivariate regression with time-series data

I am working with a dataset that includes the trajectories of various car trips and would like to be able to predict their destinations using only a subset of the trip trajectory. For instance, if in ...
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11 views

On the use of the autocovariance generating function

The autocovariance generating function is defined as: $$g_X(z) = \sum_{h = -\infty}^{\infty} \gamma(h)z^h.$$ Where $\gamma(h)$ is the autocovariance function of the considered process $X$. I can ...
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13 views

Using count data with number of days

I have two populations A and B. The data consists of count data per number of days after an event has occurred. For example: ...
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65 views

How can a univariate seasonal time series be made normally distrubuted by Box-Cox transformation?

I'm trying to fit a sarima model on the univariate data with 180 points (periodicity=12). I use the auto.arima function in R. After fitting a model to the data, the only problem is the violation of ...
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22 views

reference for regime shifting models [migrated]

I'm looking for a good introduction to regime shifting models. It would be nice to see things like simple example of regime shifting models, ways to detect a regime shift in data, fitting regime ...
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1answer
23 views

De-normalizing Google Trends data?

Does anybody have suggestions on how to de-normalize Google Trends data? The site says that their trends are made with the following metric: The numbers on the graph reflect how many searches ...
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40 views

How to use a Hidden Markov Model to detect state in a time series?

Questions Am I right in assuming that the emission probabilities will not be following a gaussian distribution for my particular problem? Obviously, I will need to train the model for state ...
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1answer
18 views

How to compare 3D accelerometer data in time series?

I'm trying to find similarity between two time series of 3D accelerometer data: Just by looking at the graphs I can tell that red-circled parts looks pretty similar to me, but I would like to get ...
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19 views

Determining statistical significance of data changing over time

I have collected motion data from three people over two days. Each of the subjects collected motion data from themselves for around 5 hours a day, for the two days, so I have about 10 hours of data in ...
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3answers
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Putting less weight on certain data points in a series for forecasting

I have a data set that contains outliers (big orders) i need to forecast this series taking the outliers into consideration. I already know what the top 11 big orders are so i dont need to detect them ...
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41 views

How to simulate an ARCH / GARCH model in R? [closed]

Considering a standard form for a ARCH model: How can I replicate the process in R not using the functions such as garchSim? ...
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50 views

Time series and EViews

I have 3 questions on EViews: m, x, and y are three series. I have found that $m \sim I(1)$, and $x \sim I(2)$,and $y \sim I(2)$ Firstly, can I generate first degree difference by writing " d(m) " ...
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95 views

Testing for autocorrelation: Ljung-Box versus Breusch-Godfrey

I am used to seeing Ljung-Box test used quite frequently for testing autocorrelation in raw data or in model residuals. I had nearly forgotten that there is another test for autocorrelation, namely, ...
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22 views

Determining the optimal lookback length for an arima forecast

How can I determine the optimal lookback length for an arima forecast? ...
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69 views
+50

Assumptions and terminology for dynamic regression with endogenous offset ($y_t=y_{t-1}+\beta X_{t-1}+\epsilon_t$)

I'm dealing with a fairly simple time series regression model with the following basic form: $y_t=y_{t-1}+\beta X_{t-1}+\epsilon_t$ I'm assuming that observations of $y$ are known without error. $X$ ...
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17 views

Does downsampling affect trend

I have a large set of time series data for which I want to identify the trend. For trend identification I am using a Simple Moving Average (SMA) function over my raw data in order to smooth them ...
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1answer
29 views

Prediction intervals in ARIMAX accounting for forecast uncertainty in future $X$?

I have a problem with my SPSS software and ARIMAX forecasts. Consider a series $Y$ that depends on a different series $X$, which is not known in advance with certainty, but must be forecasted itself. ...
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25 views

Modeling different lag structures

I know there are various information criteria that can be used to compare model specifications, including those with different lag structures. I can easily compare the Akaike Information Criterion ...
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1answer
27 views

Describing trend magnitude or detecting trends

I have time series data. One observation per time period, about 10 observations or so. When describing the data (I am interested in descriptive statistics), I'd like to say that there is either a ...