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

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477 views

How to apply an AR(MA) model to a prewhitened signal?

I have two (vehicle velocity) signals that should consist of similar "latent" drivers, but have different autocorrelation structures. The driver-signals are quite nasty statistically, so I'm not ...
2
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1answer
182 views

Identifiability in linear regression and time series

The multivariate linear regression model is given by $\mathbf{y} = \mathbf{X}\boldsymbol{\beta} + \boldsymbol{\epsilon}$, where $\boldsymbol{\epsilon} \sim \mathcal{N}(\mathbf{0, ...
8
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1answer
10k views

Confusion with Augmented Dickey Fuller test

I am working on the data set electricity available in R package tsa. My aim is to find out if an ...
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0answers
18 views

Best step pattern for unconstrained/subsequence DTW(Dynamic Time Warping)

I am implementing an unconstrained/subsequence DTW algorithm (in R). The query of data which I am trying to find a match within the reference data is much smaller (as compared to reference). I have ...
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0answers
7 views

Linear regression (best fit line) on moving averages vs raw data?

I have a series of sets of data over a period of time, with the amount of data available being quite variable between sets. One has points for almost every day but is really quite noisy; another has ...
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0answers
18 views

Difference between SUTSE and SUR Dynamic Linear Models

I am studying time-series econometrics and in particular Dynamic Linear Models for multivariate time-series. Someone can help me in understanding which is the difference between SUTSE (Seemingly ...
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0answers
7 views

Hourly electricity demand data finding AR and MA terms

I am new to time series analysis. I have hourly electricity demand data for five years (having multiple seasonalities as daily,weekly,annually) and I want to guess the number of AR and MA terms using ...
0
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1answer
404 views

How do I perform a Wald Test with multivariate Granger Causality Analysis

I am doing a Granger Causality Analysis for three economic variables (GDP, CO2 emissions and Total Energy Consumption) of Puerto Rico. I am using a Toda-Yamamoto Procedure implemented in R R. I am ...
2
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1answer
297 views

Multivariate time series model evaluation with conditional moments

Consider multivariate time series models that estimate potentially time-varying conditional means, variances, and correlations (one type of model might be a VAR(p)+Garch(1,1)+DCC Gaussian Copula ...
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3 views

Measuring the effect of weather on retail sales

I'm currently working on modeling this as an ad hoc. Sr mgmt want to know how much of our sales growth during the year can be attributed to weather. I chose to investigate "weather" as temp & ...
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1answer
1k views
+50

How to get the true mean forecast using the Arima package with a Box-Cox transformation

In the Arima package, using a Box-Cox transformation give wrong results when later applied to the forecast method. For example, consider this data: ...
0
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1answer
260 views
0
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73 views

Significant difference between time series - Can I do this?

I'd like to know whether the solution proposed below is valid/acceptable and any justification available. We have two biological conditions, and for each condition we measured 3 time series, so at ...
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0answers
4 views

Can I have a time covariate to account for temporal trends as well as time series biases such as AR/MA?

Can I have a time covariate to account for temporal trends as well as time series biases such as AR/MA? For instance if I have a model: Y ~ X + t where t is time, then can this be an alternative ...
0
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1answer
8 views

Time Series Modelling[Issue with modelling the residuals]

I am doing the sales forecast. I found the trend and seasonality manually for my time series data. Regressed time series data against the trend and seasonality and found the residuals. The residuals ...
0
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1answer
16 views

Time as random effect or fixed effect in glmmADMB

I have a longitudinal dataset where patients have a measurement with a date, currently coded as time from end of treatment (days). Now, I want to build a model. Roughly, a zero inflated Poisson model ...
2
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1answer
296 views

Weighting time series coefficients using model's likelihood

I have a question regarding to time series forecasting. In particular I've been working with a Bayesian approach, but I think the question is independent from that. I have several time series which ...
0
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1answer
17 views

Time Series Forecasting, Log or non-Log

I have read that you should use log transformations when the fluctuations on your data are increasing over time, but what do you do if the fluctuations level out over time? A plot of the time ...
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0answers
6 views

Should we predict residual of ensemble model and add it to final prediction?

We have been doing a time series project on daily wise data. We built 5 different models SVM, XGBoot, ARIMA, KNN, ANN. We then built predictive models for residuals for all of these models and got ...
0
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1answer
27 views

Interpreting Time series regression/bivariate sorts

I am somewhat unsure how to interpret some result from an analysis that I have done on two independent variables and a dependent variable. My goal is to test whether the abnormal return difference on ...
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0answers
19 views

Is HoltWinters() inferior to ets()? [on hold]

here is a simple R program using HoltWinters() function.it gives the following error message even for 100 iterations..(but run smoothly for smaller number of iterations)..But ets() handles the ...
0
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1answer
32 views

How to determine seasonality of a binary variable?

I have a dependent binary variable Y, and an independent date variable X. I want to find out if there is any seasonality (at the year level). A few notes: The binary variable is in my model ...
1
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1answer
22 views

When to run truncated backpropagation through time in recurrent neural networks?

I'm interested in training recurrent neural networks using truncated packpropagation through time (BPTT). From Sutskever (2013): Truncated BPTT...processes the sequence one timestep at a time, ...
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0answers
8 views

Imputation for Time Series of Accumulated Value

I have a regular time series of accumulated values of a variable (usage) with some missing (sometimes consecutive) intervals. Is there an imputation method that methodologically considers this ...
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0answers
28 views

Analysis of Time Series Data in R [on hold]

I am attempting to use the 'forecast' package. I have a series of 54 monthly observations, read from a CSV file, and converted to a time series. Here is the raw data: ...
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0answers
5 views

DCC models in R: how is the first starting value chosen?

The DCC model is defined through the proxy $Q_t$ as $$Q_t=(1-\alpha-\beta) \overline{Q} +\alpha\epsilon_{t-1}\epsilon_{t-1}' + \beta Q_{t-1}$$which is then normalized to find the correlation matrix ...
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1answer
133 views

Which method to use for load forecasting

I have smart meter data set that has consumption readings collected over a year and a half for every 30 mins. What I am trying to do is short term load forecasting. The data set has just three columns ...
8
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1answer
124 views

Modelling auto-correlated binary time series

What are the usual approach to modelling binary time series? Is there a paper or a text book where this is treated? I think of a binary process with strong auto-correlation. Something like the sign of ...
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0answers
5 views

Fractional Gaussian noise, the KPSS test, and stationarity

Fractional Gaussian noise (fGn) is characterized by the mean ($\mu$), the standard deviation ($\sigma$), and the Hurst index ($H$). It's my understanding that it is stationary, for the simple reason ...
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1answer
22 views

How does trend stationary recovers from shocks in long run?

I was trying to understand difference between drift and trend wherein I came across concepts of unit roots and trend stationary. (I haven't read any books on time series, just going through web). ...
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0answers
17 views

Predicting the status of an individual across time [on hold]

In my case my training data frame is like the following : Id|Month|Status|Others 1|01/16|O|X 2|01/16|O|X 3|01/16|O|X 1|02/16|E|X 2|02/16|O|X 3|02/16|F|X 1|03/16|E|X 2|03/16|O|X 3|03/16|F|X ...
4
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1answer
94 views

Why is my kalman filter trusting so much my observations?

This question follows the one asked there. I am trying to filter an equity index (Stoxx 600) time series using kalman filter. I'm using the R package dlm and my code is inspired from the dlm ...
3
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2answers
210 views

Does Sampling Frequency matter for Time Series analysis?

I am given two time series of prices between 2009 and 2013. Price series A is weekly data, series B is monthly data. I would like to compare some basic descriptive statistics of these two time series ...
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57 views

Rolling-sample estimates of the first-order autocorrelation coeffcient - STATA [on hold]

I have to construct a measure of persistence of a time series and I want to use rolling regression to do so. In particular, I'm studying inflation persistence, and I want to replicate the following ...
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1answer
719 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 ...
2
votes
1answer
206 views

Determining order of ARIMA model using Box-Jenkins. Correct approach / argumentation?

I obtained a couple of time series from estimating my (mortality-)model which I now aim to forecast with an appropriate ARIMA(p,d,q) model, which should be chosen with the use of the Box-Jenkins ...
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0answers
16 views

Threshold cointegration

I have a panel data N=45 T=25. Engle-Granger test confirms co-integration between two I(1) variables. I would like to test for threshold cointegration between these two vars. Is there a user written ...
3
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2answers
165 views

What is the impact of management on tree mortality caused by insect pest?

I am monitoring tree death caused by insects and potential impact of human treatment on yearly amount of tree mortality in areas with and without human intervention. My data are recorded by remote ...
3
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0answers
31 views

Decomposition of SARIMA models

I use R for time series analysis. I would like to evaluate decomposition algorithms. decompose and stl from "stats" package lead ...
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0answers
34 views

What is the difference between the Monte Carlo Method in R package 'DMwR' and a normal Monte Carlo Method?

I am trying to estimate the performance of a machine learning model on time series data. I saw the example of model evaluation using Monte Carlo Estimates from the book "Data Mining With R Learning ...
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2answers
257 views

Periodogram vs. spectral density diagram of a time series

Could someone explain to me the difference between a periodogram and spectral density diagram? The first diagram is produced with this block of code: ...
2
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2answers
2k views

How to extract long run and short run coefficients from ARDL (UECM) estimates?

I have estimated ARDL(UECM) in eviews but I dont know how to specify or extract the long run an short run estimates/coefficienst? what is the standard procedure to do so?
2
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1answer
28 views

Unusual use of time series: getting xreg from a given forecast value

I recently fit an ARIMA model for some daily sales data. To account for seasonality I used various dummies in xreg for different days in the month, days in the ...
3
votes
1answer
29 views

Time series analysis of electricity load questions

I have hourly data of electricity load (MW) that span 8 months (that is, 5760 data points). I also have predictions from a regression model for the same period. My goal is: to examine some ...
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0answers
11 views

Critical point in time series and pattern comparison

I have some data from a recent experiment. Two factors, each had 2 levels, resulting in 4 conditions. In each condition there were 12 participants, so 48 participants in total. Each participant ...
0
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1answer
169 views

Holt-Winters optimal parameters with gradient descent

Can we use gradient descent in order to find optimal alpha, beta and gamma for Holt-Winters model? And more generally, are there any academic works that suggest methods for finding optimal values for ...
4
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2answers
4k views
5
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2answers
2k views

Poisson regression with (auto-correlated) time series

I have a time series dataset which shows, for each day, the number of complaints received by an organization about a particular problem. I also have a number of other time series for the same period ...
5
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4answers
3k views

Moving-average model error terms

This is a basic question on Box-Jenkins MA models. As I understand, an MA model is basically a linear regression of time-series values $Y$ against previous error terms $e_t,..., e_{t-n}$. That is, the ...
2
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2answers
225 views

Determining odd time series

I have a number of time series which are derived of same underlying data. However, the data in each comes from a different source, so they may be slightly lagged or differently enriched but ...