Tagged Questions

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

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1answer
32 views

Auto correlation function of AR(p) process

I am doing a time series course and in the theory part there are few things I don't understand.In obtaining auto correlation function for AR(p) process it is done as: AR(p)=$X_t = α_1X_{t−1} + ...
0
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0answers
15 views

Understanding / Interpreting VARselect function in R

Atm I am playing around with VAR-Models and I was asking myself how to properly use the VARselect function. My question is the following: What should I give R as y? In the Help it just states "Data ...
0
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1answer
76 views

PDF and CDF of sum of random variables with different distributions

This may sound too trivial but I am having difficulty to solve an assignment problem where I need to determine the distribution and density of a random variable $Z$ which is the sum of random ...
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0answers
9 views

Clustering cases on variables discovered in-sample via factor analysis?

My Data I have 2-hourly readings on approximately 10K sensors taken over the course of a year. The resulting time series look pretty similar day to day (though there are some longer term trends), and ...
0
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0answers
25 views

Time series: What is my natural time period?

We are modeling univariate ts with R. Sampled daily since 1-1-2013 at five observations per week. We are unclear about how to decide 'natural time period'. Until now we just assumed 260 weekdays in ...
3
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2answers
49 views

Modeling an I(1) process with a cointegrating I(1) and an I(0) variable

A colleague says that estimating the following time series model is statistically sound: $$y_t = \beta_0 + \beta_1 x_{1t} + \beta_2 x_{2t} + e_t$$ where $y_t$ is nonstationary $I(1)$, $x_{1t}$ is ...
1
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1answer
25 views

Interpretation of results for unitroot test

Let's say I have a pure random walk: ...
1
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1answer
23 views

How to estimate model where instrument is correlated with dependent variable

I have the following problem: I would like to estimate the effect of price variation caused by uncertainty on an outcome variable. P is my price, X is the variable measuring uncertainty and Y is the ...
0
votes
1answer
46 views

How to prove the siginicance difference level in this data?

I have collected a set of data of 52 weeks of actual output and demand. Actual 1100 1300 1400 1500 1600 1100 1200 1600 2100 1300 1600 1300 1600 2200 2300 1700 1800 800 1400 900 2100 1400 1800 1900 ...
0
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1answer
46 views

Outliers In Predicted Intervals

In my stats class today, the professor was showing us some output from MINITAB on a prediction interval that was calculated (from time series data). For one of the prediction intervals, MINITAB had an ...
0
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0answers
7 views

Characterizing “typical behavior” for events?

I need to build a model to characterize what is typical for a series of events, which in turn will be used to flag atypical events. As an example, think of credit card purchases (how often? what ...
0
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0answers
17 views

R implementation of Zeger's parameter-driven (latent process) approach to time series regression with count data

For time series regressions with count data, Poisson-response with log link (i.e. GLM) is widely used. However, such models often suffer from serial correlation. One approach to handle was introduced ...
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0answers
18 views

Create a damping function for discrete time series data such that values converge to constant value

I have an agent-based model where an agent predicts output and then compares that value to the actual output. How can I create a damping function of sorts that will cause the delta between expected ...
0
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0answers
27 views

fitting garch (1.1) model in r or eviews

How I can have positive GARCH (1.1) parameters value using "R" or "eviews" by taking a dummy variable. Following are my data and how the dummy looks like. So I need to calculate GARCH (1.1) parameters ...
0
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0answers
11 views

Auto covariance of an AR model

I have an AR(2) series: $X_t = 1.5*X_{(t-1)} - 1.2 * X_{(t-2)}-3*\epsilon_{t}$. Given $X_0 = 0, X_1$ ~ N(0,1). Then Cov($X_2, X_3)$ = $Cov(1.5*X_1 -3*\epsilon_2, 1.5*(1.5*X_1 -3*\epsilon_2)-1.2*X_1 ...
1
vote
1answer
60 views

Arima time series forecast (auto.arima) with multiple exogeneous variables in R

I would like to conduct a forecast based on a multiple time series ARIMA-model with multiple exogeneous variables. Since I am not that skillfull with regards to neither statistics nor R I want to keep ...
5
votes
5answers
135 views

What's the difference between time-series econometrics and panel data econometrics?

This question may be very naive, but the way I'm taught econometrics I'm very confused if there's a difference between time-series and panel data method. Regarding time series, I've covered topics ...
1
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0answers
50 views

time series forecasting with 53 weeks in a year

I am building a time series model in R. I have four years of data from 2010 to 2013 and doing forecasting fro 2014. According to the calendar that my organisation follows, In 2014 , there would be 53 ...
0
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1answer
48 views

OLS versus ML estimation of VECM

A vector error correction (VECM) model has an equivalent vector autoregression (VAR) representation. (VECM) $\;\;\;\Delta y_t=\Pi y_{t-1}+\Gamma_1\Delta y_{t-1}+...+\Gamma_{p-1}\Delta ...
0
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1answer
18 views

Compute frequency of time series

I would like to understand how is the period or frequency of time series calculated. Shouldn't a weekly repeating pattern be of the frequency 7, and the yearly pattern be 365? I ask because the paper ...
1
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0answers
11 views

Proportion (difference) test for drifting proportions

Two machines are spitting out coins (at different non-constant rate). The probability of flipping heads for every machine is believed to change very slowly in time (compared to flipping rate). It is ...
1
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1answer
40 views

TBATS: why set seasonal periods?

While trying to estimate the level, trend, and seasonal components with the TBATS model (forecast pkg in R), I notice that the ...
0
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0answers
22 views

What is variance and co variance related to time series?

I'm trying to understand the Mahalanobis distance method which makes use of a covariance matrix. However i am not clear about the idea of variance and covariance with respect to time series. And also ...
0
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0answers
45 views

3 month forecast for commodity prices in R - general help for approach

In the following I'll describe my undertaking as detailed as possible in order to provide you enough information. Please keep in mind (when answering) that neither I'm a matematician nor a ...
1
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0answers
52 views

Analyzing time series in depth

I want to create a code which tests absolutely everything for time-series forecasting accuracy. The current tests that I do are: bptest() - tests against ...
0
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0answers
18 views

Initializing in arima.sim in R [migrated]

I want to simulate from an ARMA(1,2) series. I need to initialize $X_0$ = 9. How do I do it in R? I cannot find any provision for initialization in arima.sim
1
vote
1answer
63 views

Weekly seasonality model by ARIMA+Fourier terms+dummies

This is a long post but it is not conceptually difficult. Please bear with me. I am trying to model the seasonality of production volume of an agricultural commodity. I do not care about the ...
3
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0answers
39 views

Methods of fitting a dynamic linear model

I'm taking a time series course and am learning about exchangeable time series form of dynamic linear models (DLMs). This is given by: \begin{align*} \mathbf{y}_t' &= ...
4
votes
3answers
44 views

Cointegration - Why can't I estimate a VAR on the differences?

When talking about variables that are I(1) (the first difference is stationary), Lutkepohl book says: "...in general, a VAR process with cointegrated variables does not admit a pure VAR representation ...
1
vote
1answer
57 views

What are the implications of estimating a covariance matrix from a correlated sample?

Given a sample of $n$ independent observations $x_1,...,x_n$ (where $x_i$ are $p$-dimensional column vectors), the $p \times p$ sample covariance matrix is defined as ...
2
votes
1answer
67 views

Time Series Function - Constant vs Piecewise

I have daily data for online marketing $ spend and the number of clicks to the website gained. I want to determine a function that 'maps' the two together. I cannot use normal linear regression ...
1
vote
0answers
37 views

Fit a VAR model with R

I have a bivariate time series z_t where z_1t is the change in monthly US treasury bills (maturity 3 months) and z_2t the inflation rate,in percentage, of the U.S. monthly consumer price index ...
0
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0answers
21 views

Calculating euclidian distace among z-score values

First of all I should say I'm from a biological domain. I'm trying to cluster web visitors based on the time they spent on each web page. Let's suppose my raw data set is in the following ...
1
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1answer
62 views

Definition and proof of Strict Stationarity

The definition of strict stationarity I'm using is the following: $(X_1,...,X_n)=^d(X_{1+h},...,X_{n+h})$, for any integer h, and positive integer n. I'm trying to prove that ...
1
vote
1answer
70 views

What are some useful robust and scalable approaches towards anomaly detection of a time series data?

What are some useful robust and scalable approaches/methods towards anomaly detection of a time series data? I am mainly looking for some practical approaches carried out using Python, R, Java, etc. ...
0
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0answers
20 views

Correlation index for correlating high volume of timeseries

Is there any way to pre-build some "correlation index" and save it to DB for any given timeseries. For example: ...
0
votes
0answers
51 views

Should I use stationarity test before OLS regression

I need to know if conducting a stationarity test on the variables, such as the Dickey-Fuller test, is important before doing any regression like OLS? if so, if the variable is stationary after ...
0
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0answers
24 views

Time series forecasting with multiple series with constraints

Hello and thanks in advance. I am using ARIMA or VAR models to forecast sales revenue. Suppose I have three different time series in each of three categories (making 9 series in total). The first ...
0
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0answers
16 views

Infer significance (in trends) in time-series data

I got a dataset with multiple time variables (i.e. 21 fixed measurement times). It is expected that a (downward) trend over time can be discerned in these variables. When plotting scatterplots I find ...
3
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2answers
34 views

What is the main idea behind the power spectrum?

Assume that we have a time series and we have calculate the corresponding auto-covariance function. Having the auto-covariance function we can calculate the corresponding power spectrum and having the ...
0
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0answers
5 views

Daily time series forecasting by item in R? [migrated]

I have a collection of sales data by item in this format ...
-2
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0answers
17 views

Forecasting in R X Axis [migrated]

Good day How do I change the x axis so that it shows the year and month? At the moment the x axis doesn't look right and comes up with 2014.0, 2014.5 and 2015.0. I want to use the forecast package ...
2
votes
1answer
46 views

Asymptotic distribution of a recursive statistic

I have a (time series related) test statistic which is asymptotically normal. I would like to know what is the asymptotic distribution of its maximal value obtained by a recursive estimation. For ...
0
votes
2answers
52 views

What is the purpose of Leads and Lags in a time series?

I will analyse stock prices and i don't understand the purpose of leading and lagging. can you please suggest me some preliminary analysis on the stock prices also, this will help me for my thesis.. ...
0
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0answers
30 views

clustering analysis of large amount of time series

I would like to cluster a set of time series, which are composed of around 50000 different time series. Are there established algorithms/package that can handle this scalability problem?
1
vote
2answers
117 views

Determine when time-series should be logged (or any other transformation) and applied automatically

Is there any way to test whether a series should be logged or transformed in another way? I have a code of which i use to run lots of different data through to forecast. Some of the data definitely ...
0
votes
1answer
32 views

AR(2) & constant & trend - very poor constant estimates?

Here is a problem that was puzzling me. Suppose I simulate the AR(2) process with constant and trend using the code below (I apologize for inefficiency and inelegance - the aim was to get job done at ...
2
votes
1answer
57 views

Confusing Holt-Winters parameters

I have got a model for forecasting using holt-winters. However the parameters confuse me... The parameters show that there is no trend or seasonality even though there is definite trend and ...
3
votes
3answers
176 views

How do I handle nonexistent or missing data?

I tried a forecasting method and want to check if my method is correct or not. My study is comparing different kinds of mutual funds. I want to use the GCC index as a benchmark for one of them but ...
0
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0answers
18 views

how could i handle with missing data or non existent data? [duplicate]

i tried a forecasting method and i want to check if it is correct or not and why? my study is about evaluating mutual funds for two kind of them it is a comparative study and i wan to use gcc index ...