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

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Determining the effect of number of likes

Let's say I have marketing data and I need to determine how effective the marketing is. The marketing strategy is to publish facebook posts at inconsistent intervals. The goal is to see how the ...
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Property of the autocovariance function in time series

In the framework of time series analysis Why does $n^{-1} \sum_{|h| <n} |\gamma(h)| = 2|\gamma(n)| $? Where $\gamma(h)$ is the auto-covariance function defines as $\gamma(h) \equiv Cov(X_{t+h}, ...
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A continuous function of a sequence of random vectors converges in probability to the function of the limit

Proposition: If $\{ X_n \}$ is a sequence of k-dimensional random vectors s.t. $X_n \overset{p}{\to} X$ and if $g: R^k \rightarrow R^m$ is a continuous mapping, then $g(X_n) \overset{p}{\to} g(X)$. ...
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Fitting a CGARCH-M model

I'm dealing with a CGARCH-M model that the long and short-run volatility components have different effects on returns. Here are its mean and variance equations. Mean equation: y_t =α+βx_t + γ_1 ...
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25 views

Principal Component Analysis on Time series data and panel data

I am trying to build an index on infrastructure and compare the index over the years and between nations. Since the variables are highly correlated with each other, review has suggested me to proceed ...
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17 views

what is the size of data should be predicted to make the predictive model valid

if I have time series with 1000 values , and I want to build a predictive model , how far in the future should i successfully forecast to make my predictive model valid, is there any condition or rule ...
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25 views

Correlogram and ACF/PACF applied to US index of unemployment rate

This eviews workfile contains US index of unemployment from 1960 to 2008 quarterly. I'm trying to understand ACF and PACF. Below is a correlogram for the first 24 lags: What can the correlogram ...
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85 views

Making sense of the first difference regression model

There must be a fundamental error in my approach. Let's start by stating we have a simple regression with two variables $X_t$ and $Y_t$: $Y_t = BX_t + e_t$ Where $B$ is the coefficient and $e_t$ is ...
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28 views

Interpreting the result of decomposing time series

I don't have a lot of experience working with time series data. Now I have a 3 year, monthly data for several entities (you can think about them as different stores), that I would like to do some ...
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1answer
17 views

Is series cointegrated if residual is stationary under time-varying coefficient regression?

Traditionally, if $x_t$ and $y_t$ are both $I(1)$, they are cointegrated when there exists some linear combination $z_t=y_t-$$\gamma$ $x_t$ such that $z_t$ is stationary or $I(0)$. My question is if ...
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30 views

How to use SVM to do time series prediction?

I want to know how to use SVM to do time series prediction? what the differences of input vecvtor X of our model between time-series prediction and standard kernelized regression problem?
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31 views

What is the meaning characterizing a model as “conditional mean”

I've been going through time series material of late, trying to re-invent myself as a practitioner in the field. Until I got to the point of actually trying to Matlab some models, I had never run ...
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31 views

Best way to account for time lags in logistic regression (GLM or GLMM)

I am trying to determine the best, most conservative way to account of time lags in a logistic regression type analysis (a generalized linear model with or without mixed effects). I am working with ...
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14 views

Difference between autocovariance and autocorrelation [on hold]

I read from a book when i read about Yule-Walker estimation that "autocorrelation has nicer properties than autocovariance".. can any one tell me why please
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15 views

Need an effective way to show distribution changes over time and outlier reoccurence

Does anyone have suggestions on the best way to approach this problem? I have a large dataset (over 200k+ per day) in a MySQL database, that consists of a single record per user per day with a ...
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26 views

Finding the optimal combination of independent variables for a constrained dependent variable

I'm currently working on power plant time series data and my main objective is finding out the optimal combination of independent variables which would keep "SO2 concentration (dependent variable) ...
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1answer
16 views

Seasonal component in irregular time series

Is it possible to identify seasonality in a Time Series that is irregular? I am currently working with a time series that is being periodically constructed (via crawling), but these crawled ...
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7 views

How can I find Matching between a data set with given data set groups

I have a variability data new one, I would like to find the matching between given data set with group of data sets, which one among the data sets group has matched with the new data set. eg: A, B, ...
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1answer
23 views

How do I obtain the “Anomaly series” of a time series?

I have a time series of the Sea Surface Temperature (SST) of the Caribean Sea and I have to obtain the anomalies of that time series. ...
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1answer
28 views

How to add time dummies in a regression?

I would be grateful if someone teaches me how to use time dummies in a regression to capture the effect of introducing a law. I am doing a study on reforms by assessing the pre and post effect of a ...
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32 views

Curve fitting in R

I had 4 groups of data (in color 1 to 4) and one group is the data for one day, so I had 4 days of data. I was trying to fit a line which describes the pattern of theses lines (oscillating pattern) ...
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23 views

Time series forecasting use SVM

I am trying to set-up a python code for forecasting a time-series, using SVM libraries of scikit-learn. My data contains X values at a day interval for the last one years, and I need to predict y for ...
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8 views

On Estimating the spectral density with a weighted sum of the sample covariances

I am new to estimating the spectral density and would like a reference that demonstrates that taking a weighted sum of the sample covariances of a sequence of covariance stationary random variables ...
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deletion diagnostics for time series

I'm following a tutorial on time series regression, which discusses diagnosis through selective deletion of data across the entire set of predictors, one observation at a time: ...
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1answer
37 views

What is the long run variance?

How is long run variance in the realm of time series analysis defined? I understand it is utilized in the case there is a correlation structure in the data. So our stochastic process would not be a ...
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26 views

How to find AIC, BIC values in Johansen cointegration test in R software? [on hold]

In order to take the best lag for Johansen cointegration test (trace) we have to take the lag with minimum AIC and BIC values, right? In R, I have used the syntax ...
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36 views

Time Series Analysis using Fourier Technique

i need to perform "Time Series Analysis" using Fourier Analysis/Technique on temperature data of 17 years. Their are four columns in it "Years", "Months", "Days" and "Temperature in C". I need basic ...
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7 views

Hedge ratio formula

Help to clarify this fact. In the simplest case, in order to find the hedge ratio using a linear regression of the form: $S_t = \alpha + hF_t + \epsilon_t$, where $h$ is the hedge ratio or the slope ...
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1answer
38 views

A quick question about time series forecasting

I have collected daily sales data X(t) and Y(t) over two different areas . Total sales Z(t) ...
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1answer
44 views

Understanding the Durbin Watson test

The test statistic for the Durbin Watson test can range from 0-4 from what I have gathered. Now the lower limit of 0 makes sense considering the test statistic consists of two summations which are ...
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12 views

Statistically test difference based on time series

Usually in statistical hypothesis testing, we randomly split some unit into treatment group and control group, and we test if there are difference between treatment group and control group based on a ...
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19 views

concept drift detection

I'm working on a project that involves concept drift detection for a time series. Are there any well-known techniques/methods/algorithms that are known to be effective for this sort of problem? ...
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13 views

Aggregated probability using irregularly-spaced time series data

I have dataset describing a group of animals' size and growth over 2 years. These particular animals grow in non-continuous growth steps and I wish to model the frequency of these growth steps during ...
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Bad results for R's auto.arima

I have a time series for sales data on a weekly and monthly basis. I tried using holt.winter and auto.arima. ...
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Starting-point of time-series influences regression?

I've used tslm() under the R-package fpp to analyse two time series, which seem similar: ...
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28 views

compare time series data with ODE simulation

The same experiment was performed for 4 different initial conditions $(j=1,2,3,4)$. For each initial condition, there were 3 repetition $(i=1,2,3)$ of the experiment. I have 4 sets of data: $X_{ij} = ...
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22 views

Differing frequencies for time series ts() function in R

I understand the frequency argument of the ts function in R is set to work for monthly, quarterly, yearly data, etc. In my case, ...
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17 views

Tune a neural network and prevent overfitting

I'm using a neural network for the first time and I would like to know if I'm doing this right. I'm working with time series for 5 years, and in each year I have a total of 18 time series plus the ...
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Estimating AR process for Logistic Regression

I'm fitting a time-series model with independent $X$ variables coded as months of the year (so there are 12 of them) and the dependent $y$ variable is some proportion, bounded between 0 and 1. As a ...
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12 views

What is the correct procedure for conducing a Johansen Cointegration Test?

As it seems to say in Walter Ender's Applied Econometric Time Series, I'm doing the following: First, I do believe one should estimate a VAR model on the levels of the data and then proceed to test ...
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23 views

Metric for estimating declining or ascending trend in timeseries? [closed]

I have a large number of ECG timeseries data from multiple volunteers. I calculate the signal quality in any of the given dataset by estimating the quality of the signal in a non-overlapping window of ...
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40 views

Finding algorithm to detect anomaly in non gaussian data

I have a data (time series like CPU, traffic and so on) that doesnt have a normal distribution usually (especially when I'm looking at 1 hour data). Are there any algorithm to find anomalies? I ...
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“Multi-Task” Logistic regression with time series data [closed]

I'm trying to create model for consumer loan defaults that incorporates individuals payment behavior as time series. Typically this kind of problem is modeled using Cox/Allen model. Then, the other ...
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21 views

What non random patterns in a series Autocorrelation cannot detect

I know there are complex patterns in a series that cannot be detected by autocorrelation... but I cannot find what types of patterns these are. Can anyone provide an instance where the autocorrelation ...
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33 views

Measuring the change of an increment in time series

Assume that two series ($x_1,\dotso,x_n$) and ($y_1,\dotso,y_n$) are linearly correlated. What is the connection between $y_j-y_i$ and $x_j-x_i$ in terms of Pearson's $r$ and the variance of $x$ ...
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12 views

Hedge ratio with VECM DCC GARCH

For asset pairs -- spot and futures -- I need to find the hedge ratio. To find the hedge ratio I need to use two models: VECM for describing the dynamics of spot and futures. On the basis of the ...
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1answer
45 views

Breusch-Pagan Test for ARIMA Model in R

I am testing my model using the Breusch-Pagan Test, but have not been able to find anything online regarding how to calculate it for an ARIMA Model. My AR1 Model is: ...
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22 views

Calculate correlation between events and signals in time series

I have a time series signal (continuous values) and some events occurring. An event for me is characterized by the fact that it only has a single timestamp assigned to it and not a timespan. Is there ...
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35 views

Estimation of a VECM model

I am attending a time series econometrics course and I am working on VECM models. We have learnt that to estimate a VECM model we should use Engle-Granger two-step procedure but I have not understood ...
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How to evaluate Features for Time Series

I am new to time series and have a few question regarding evaluating and benchmarking my features for a time series model. The question I am trying to answer is whether my social media features ...