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

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Markov Switching Forecast. How can I derive this?

Consider the autoregressive model, $\left[ \begin{array}{l} y^{\ast}_t\\ x_t^{\ast} \end{array} \right] = \left[ \begin{array}{l} a_{11}\\ a_{21} \end{array} \begin{array}{l} a_{12}\\ a_{...
4
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2answers
328 views

Regression analysis for more than one categorical variable in time series

I have a time series data for shipment with following variables: Year: 2008, 2009, 2010, 2011, 2012, 2013 Month: jan, feb, ..., dec Number of ordering days Shipment Volume I want to know the ...
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1answer
732 views

Is ARMA model possible for series with non significant ACF/PACF?

I am playing around with SP500 data from the MASS package. From observation of the ACF and PACF there seem to be no significant autocorrelation. Now I want to model ...
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5answers
414 views

Can you develop an econometrics model for stress test purpose only focusing on 2008-2009 data?

I have become aware that a group at a large corporation is developing an econometrics model to forecast sales of their product. They are using this model solely to estimate sales in specified stress ...
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1answer
439 views

How to model time-series data in CRFSuite?

I recently came across the CRFSuite package for CRFs. Though, it is primarily used for NLP applications like POS tagging, i was wondering if I could use it to model time-series data as well? Have any ...
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1answer
412 views

Replicating bgtest from lmtest

Cross-posted from SO. I am trying to replicate the results of bgtest from the lmtest R package. I am using the following ...
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2answers
1k views

Autocorrelation and trends

What is the relation between the autocorrelation and the trend? Can a trend exist in a time series of independent variables? And in time series with a non-zero autocorrelation, does a trend always ...
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2answers
106 views

Cancellation operation in time series analysis

When studying the time series analysis, I read the following example. I do not know how to understand this cancellation process. Yes, it can be cancelled like normal algebraic formula. But this “z” ...
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1answer
292 views

Practically handling many non-stationary forecasting predictors

This question is about specific strategies to deal with non-stationary variables in forecasting. This problem usually rears its ugly head when you have a predictor whose levels are relevant to the ...
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1answer
190 views

Estimating hidden transfers of market share

Suppose we have yearly data representing the market share of three companies, say A, B and C. In other words, we have observations: $$ A_t, \; B_t \;\; \text{and} \;\; C_t \;\; \text{where} \; \; ...
2
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2answers
945 views

Variable selection in time-series forecasting

I have a time-series forecasting task and would like some input on variable selection and regularisation. My problem has the following characteristics: 2,000,000 sample size. Most of the time, no ...
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1answer
3k views

Intuition behind cross-correlation function interpretation vs. correlation of lagged time series

Can someone please explain the difference behind WHY the cross correlation function ccf() chooses to keep the same denominator for all lags and chooses to ignore ...
2
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1answer
127 views

Detecting the interdependence of autocorrelated sequences

It is easy enough to detect interdependence of two discrete-time white noise sequences - one just takes cross-covariance and compares it with variance. But what if both time series are "non-white", ...
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245 views

What statistics are preserved under aggregation?

If we have a long, high resolution time series, with lots of noise, it often makes sense to aggregate the data to a lower resolution (say, daily to monthly values) to get a better understanding of ...
5
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1answer
188 views

Spurious correlation

I've read that if two time series, $Y_t$ and $X_t$, are trend stationary, then regressing $Y_t$ on $X_t$ results in a spurious regression because of an omitted time trend variable. Let $Y_t = \delta_0 ...
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1answer
78 views

Beginner question: available space in a ware house

I have a year's worth of data for the available slots in a warehouse. There is a data point for every minute of the year. timestamp available slots The data seems to follow a certain pattern, ...
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1answer
2k views

How to simulate two correlated AR(1) time series?

Is it possible to simulate two time series AR(1) for example 0.5 and 0.8 and at the same time these time series to be correlated with $\rho = 0.8$ using R? ...
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26 views

One sided estimates

I am looking for a method to estimate an unobservable process in the following set up: at some moments of time (which I don't control) I am getting an observation, that the value of the process is ...
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430 views

Evaluating Time Series Prediction Performance

I have a Dynamic Naive Bayes Model trained on a couple of temporal variables. The output of the model is the prediction of P(Event) @ t+1, estimated at each ...
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6answers
1k views

Measuring the smoothness of time series

I work on a method that gives a (noisy) estimation of brain volume over time in Alzheimer's patients. As we know that the evolution is smooth and even mostly linear if looked at over a time frame of ...
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107 views

Stationarity check

My panel regression model is as follows: $$Y_{it}=PS_{it}+PF_{it}+EF_{it}+ \mathbf X_{it}+e_{it}$$ where $i$ : country, $t$ : year, $Y_{it}$ : GDP per capita, $PS_{it}$ : Political stability, $...
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1answer
2k views

Time-series machine learning methods and R packages

I am trying to determine how to use machine learning models such as for eg., random Forest with (non-financial) time-series data. Using an example, suppose we wanted to find based on monthly scores ...
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1answer
144 views

How to compare rates of occurence in consecutive time series count data?

My data consists of occurrences of words in time windows. E.g.: Day; Word; Frequency 1; "dog"; 45 1; "cat"; 2 ... 2; "dog"; 90 2; "cat"; 4 ... I would like to ...
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2answers
2k views

How to approach forecasting time-series data

I'm statistics newbie and any help in picking a good method to analyze the data that I have would be very welcome: We have a customer that has an active Facebook page, that gets posted on regularity. ...
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2answers
1k views

What is the definition of one-sample Kolmogorov–Smirnov test?

I have a time series data set of photon arrival times from a detector and need to know whether the arrival time is uniform.It is a continuous distribution? I have calculated the maximum $D$ between ...
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4answers
7k views

Are models identified by auto.arima() parsimonious?

I have been trying to learn and apply ARIMA models. I have been reading an excellent text on ARIMA by Pankratz - Forecasting with Univariate Box - Jenkins Models: Concepts and Cases. In the text the ...
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80 views

converting back to raw/original scale from time series tranformations and standard deviation

If I have a time series of Xt observations. I convert them to returns by: Rt = Ln(Pt) - Ln(Pt-1). I then calculate the 20 period moving average and subtract from ...
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1answer
2k views

Analysis of a time series with a fixed and random factor in R

I have a dataset which I am not sure how to analyse. The dataset came from the following experiment: I grew plants (2 different types) and measured their height at different time point (each plant ...
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4answers
1k views

Regression of data that includes a date

I have a dataset that contains a few hundred transactions from a three suppliers operating in 100+ countries over a three year period. We've found that the country of sales is not a significant ...
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122 views

$R^2$ from a regression of two trend-stationary processes, $Y_t$ and $X_t$

In Estimation and Inference in Econometrics, by Davidson and MacKinnon, p.671, they claim that $R^2$ from a regression of $Y_t$ on $X_t$, where both time series are trend stationary, tends to 1 as $n$ ...
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1answer
191 views

Estimate a model by minimising the sum of the one-, two, … and h-step ahead forecasts?

When fitting (stationary) time series models, such as ARIMA models, the standard approach is to minimise the one-step ahead forecasting error, which is equivalent to performing maximum likelihood ...
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1answer
359 views

Spatial autocorrelation (SAC) while analysing survey data

I am confused about some aspects of spatial autocorrelation usind survey data (survey which is repeated every year). I have data from 1991 to 2012 with sampling region pretty consistent every year. I ...
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624 views

How to prove that this process is strict stationnary

$W_t$ is strict stationary process and $X_t=g(W_{t-1},W_{t-2},...,W_{t-q})$ where $g$ is a measurable function. Prove that the process $X_t$ is strictly stationary. Remark : to prove that a process $...
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235 views

Optimisation model paramaters for time series data using k-fold cross validation

I'm trying to optimise model parameters for times series data using k-fold cross-validation. I'm using the forward chaining strategy described here: Using k-fold cross-validation for time-series ...
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2k views

Arima residual calculation and comparison with R

I have simulated an ARIMA(1,0,1) process using R. Below is the code. ...
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1answer
59 views

How to determine what method is being used?

I was wondering what time series model would forecast future values in such a static but decreasing manner? ( I'm talking about the values around where the circle appears, as apparently, the data set ...
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4k views

Is two-way ANOVA appropriate?

This is the description of my study. I'm experimenting with three plants: A, B, and C. These plants are supposed to reduce blood glucose for diabetic patients. I want to determine which of these three ...
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1answer
272 views

Is it possible to compare time series method with non-time series method?

Is it make sense that time series method to be compared with non-time series method ? and if it is possible could somebody tell me which non-times series method can I apply to make comparison between ...
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1answer
312 views

How to visualize changes in time series variance

I am exploring a time series with the aim of detecting obvious changes in variance over time (I have little experience in time series analysis). The hypothesis is that the series has become more ...
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1answer
559 views

Handling missing data (holidays) in multiple time series (historical simulation VaR)

I have two time series of daily returns on two stock indices (S&P 500 and BOVESPA) that I would like to estimate the portfolio value at risk (VaR) for. Since these are indices from two different ...
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What are all the good qualities of an ideal forecasting method?

I began learning and applying forecasting at my job few months ago. Forecasting in real world practical problems are really challenging unlike textbook exercise. Below are from my perspective some of ...
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1answer
356 views

Moving average ARIMA error term

Lets say that I have a time series data $Y_{t}$. I'm trying to forecast using am moving average MA(1) model using Box Jenkins methodology. The following is the equation for an MA(1) obtained from a ...
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1answer
1k views

Understanding the SVAR model

I have difficulty understanding the Structural Vector Autoregression (SVAR). I have some books about it, and have read them, but still cannot grasp the idea behind that concept. Can someone explain me ...
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1answer
2k views

How to demonstrate that this process is weakly stationary?

Given $X_t=\sin(2\pi Ut)$ with $U$ uniformly distributed over $(0,1)$ and $t$ integer, how can I prove that $X_t$ is weakly stationary? (I didn't manage to calculate the correlation because I didn't ...
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2answers
266 views

Generating random numbers based on partial correlation data

I need to generate random numbers based on already existing partial correlation data (not correlation or covariance data). Specifically, a 168*12 matrix based on a 12*12 partial correlation matrix. ...
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1answer
396 views

How to use error term in AR (2) model for predicting future values?

We use turbidity to estimate suspended-sediment concentration (SSC)- our data was serially correlated. We ran an ARMA process and ended up with a AR (2) model. Our equation in log form is: estlogi(...
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1answer
1k views

LIBSVM “Warning: using -h 0 may be faster”

I am using LIBSVM package (Matlab version) in order to perform wind power forecasting. My training dataset is quite big, approx. 52000 values of 4 parameters. The ...
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2answers
1k views

How to construct a rolling annual returns from a time series using R?

I have the daily closed values of the initial index for DJUSER, MSCI, SP500, SPGSCI from 1 January 1999 to 31 December 2011. I want to transform them in to data of rolling annual returns. How to do ...
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349 views

Time series dynamic poisson regression

I have a time series count data by customers that I would like to regress on past months items (count) sold and promotional effects (current and past). Below is an example, and the dataset has one ...
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1answer
1k views

Interpreting the coefficients of ARCH Lagrange Multiplier Test

I am new to econometrics and I am building my first econometric model. I ran the LM test on a univariate time series data of 12000 observations and got the following stats: ...