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

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Non-fixed seasonality in time series

I believe some time series data I am looking at shows seasonality according to general elections (UK, so this can be 4 or 5 years). How do I remove this effect in R?
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11 views

Cross correlation function plot

I'm a bit unsure how to interpret ccf plots. Is this done exactly the same way as acfs? For example, a slow decaying ccf means there is trend between the 2 time series? Can someone explain in terms ...
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1answer
55 views

Time series trend

I have a time series which has a very strong upward trend for the first half, then very strong downward for the second half and finishes pretty much back where it started. Should I split the data in ...
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6 views

automation code for dynamice regression with arima in r [migrated]

I want to make automation code for dynamic regression with arima..where i just have to insert dataset and all calculation will be done automatically and store model,graphs,MAPE,etc below are the ...
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108 views

Fitting ARMA model with MATLAB R2012b

I want to fit an ARMA model on a time series (quarterly log returns of a 10 year bond) using MATLAB R2012b. This is part of an exercise. I have problems with the code and the interpretation of a ...
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1answer
32 views

Estimation of residual in ARIMA model

How do I estimate the residual $\varepsilon_{t}$ of a Seasonal ARIMA model $\hat{Y}_t=\hat{\phi}{Y}_{t-1}+\hat{\Phi}{Y}_{t-12}+\varepsilon_{t}$? If the MSE is 0.114, what does it mean?
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43 views

Statistical comparison of two signals

I need to develop an algorithm that will compare two signals and generate some metric(s) to describe changes between them. Signal processing and analysis isn’t my strong point so I would appreciate ...
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28 views

ar.ols() or arima() for modeling time series

When trying to fit a AR(p) model to a time series in R, it seems that both ar.ols() and arima() will work. Is there some consideration of when to use which then? ar.ols() seems to use least square ...
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20 views

How can I statistically compare two time-series? [duplicate]

I have two time-series. Data have been logged over time for two different objects. Is there a significance test, applicable in SPSS or Minitab, that can be used to determine whether there is a ...
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1answer
42 views

Variance of sample mean in an AR(1) process

I learned that for an AR(1) model $x_n = b + \phi x_{n-1} + a_n$ with $|\phi| < 1$ and $a_n \sim WN(0, \sigma_a^2)$, the covariance between $x_n$ and $x_{n+h}$ is $$ \mathrm{Cov} (h) = ...
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1answer
47 views

Separating Base and Promotional Volume

I am working on a project where I have to separate base and promotional volume from the sales data. I have sales data for the last 4 years at week level. How can I separate base and promotional volume ...
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28 views

How to find the most important factors or combination of levels in a finite data set

I’d like to discover which “calendar factors” (e.g. day of the week, month) have the strongest relationship with whether or not a particular product is sold at least once. For days when the product ...
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9 views

parameter estimation in bivariate autoregressive sem with individual heterogeneity - troublesome likelihood surface

I am working with models for panel data of individuals, wherein: my latent variables Yt= $\beta$Yt-1+Ai+Q Individuals level of the process A~N(l,h) Innovation of the latent Q~N(0,q) observed variables ...
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1answer
35 views

How do I calculate the distribution of number of events in the busiest period?

I've got an estimate of the number of site visitors I'll see in a 1 hour period clicking email links in a large email campaign. I need to make sure I've got the required server capacity. That means ...
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14 views

t-test correction for serial correlation

I have a time series of serially correlated data (differences between spatial means of meteorological variables). I calculated the lag-1 autocorrelation (rho), and corrected the number of degrees of ...
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31 views

Question about trend and predictions in R

I have weekly data which I am considering as reference. So Mondays trend for example.. orders will kind of serve as valid data to make predictions for orders throughout the day for every Monday. I ...
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24 views

How to find a conditional probability using copula-based Markov process?

I have a monthly time series of a water quality parameter. I used copula-based Markov process of C(Y(t), Y(t-1) and I forecasted the mean behavior of Yt by following equation: Now, I need to find ...
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27 views

Arima modelling estimate in SAS and find out if model is adequate or not

I have been attempting to do an ARIMA modelling in SAS. The series is not stationary but when I estimate the ACF and PACF p-values I don't get appropriate answers to find out if my model is adequate ...
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1answer
88 views

R time-series forecasting with neural network, auto.arima and ets

I've heard a bit about using neural networks to forecast time series. How can I compare, which method for forecasting my time-series (daily retail data) is better: auto.arima(x), ets(x) or ...
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46 views

ARIMAX for modelling daily sales

I am trying to model daily sales for a take out restaurant. They are only open on business days - no holidays or weekends - as their primary clients are office workers on their lunch breaks. Below is ...
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14 views

Shrinkage estimation for regression with ARMA errors

I was wondering if someone knows a R-package or function library for the topic of shrinkage for regression with ARMA errors. Please let me know if you came across something related. Thank you! ...
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55 views

Predicting Next Likely Outcome of Binary Time Series?

I'm trying to approach the following problem: Danny & Johnny are professional basketball players. Each day they meet, and play for a while. Whoever scores the most points is declared winner for ...
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112 views

Forecasting daily data with trend, yearly, day of the week, and moving holiday effects

I'm expanding a question I posed earlier because I think it was lacking detail. I'm attempting to forecast daily demand for a restaurant that sells take away food, primarily to office workers on ...
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24 views

Daily Sales Data - Week Days Only, No Holidays

I'm trying to predict the daily sales for a take out restaurant. They are located in the downtown core of a large city. Their primary customers are office workers on their lunch breaks, and as such ...
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1answer
177 views

Time series forecasting using R

I have many time series(retail data). Some with trends, some seasonal, and some with neither. With period day, week or month. I need to make forecast, for each time serie. I'm looking for the most ...
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22 views

lagged covariate or time series for analyzing change over time in Hgb (limited observations) in the prediction of disease status

I want to test whether changes in Hemoglobin (Hgb) levels over time can help diagnose Myelodysplastic syndrome (MDS). I have a cohort of thousands of patients. Each patient has several Hgb ...
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19 views

Time Series Forecasting Method to use both Predicted and Predictor variables

I am learning Predictive modeling and building a Forecasting model to predict Insurance sales in US as a part of my academic project. I want to do Time Series forecasting. I have Y(t) as my response ...
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1answer
65 views

Forecasting irregular time series (with R)

There are several methods to make forecasts of equidistant time series (e.g. Holt-Winters, ARIMA, ...). However I am currently working on the following irregular spaced data set, which has a varying ...
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1answer
31 views

Difference between iid data and non-iid data for a simple regression problem

I am trying to understand the difference between iid and non-iid data. Let's consider a given time series, and say it's reasonable to assume that at each time point the random variable $X_t$ depends ...
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19 views

R: One period our cross validation with time series

I have quarterly data with one causal variable (X) and one dependent variable (Y). 30 such observations. I have the X variable for a quarter, and I'm seeking to predict that quarter's Y. The ...
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1answer
50 views

Warning message in auto.arima

I am using auto.arima() for prediction, and getting the following warning message. I want to know if I can ignore this warning message or if I should be worried. ...
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1answer
112 views

Time series prediction: visualising path uncertainty region

I am predicting a time series' future evolution and am evaluating the path uncertainty using bootstrapping. Is there a good way to visualise the uncertainty that goes beyond simply plotting a pair of ...
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34 views

Keywords to find academic lectures on multivariate time series analysis on youtube

If I search for "HOTT homotopy type theory" on youtube, I find numerous (advanced/state-of-the art) academic lectures on the topic. For instance the following lectures are found on youtube: ...
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ARDL, Lag Terms and Singularity

I am interested in fitting an ARDL model that has 4 lags for each explanatory variable. However, when I fitting the model in R. R says that coefficients are not defined because of singularities. Is ...
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1answer
15 views

Calculating phi11 (or phi22) from an MA(1) process

I've come across a question where I have an MA(1) process like so: $X_t = b_t - 0.4 b_{t-1}$ (where $b_t$ is a white noise process and $t$ is the time index) The question asks me to find $\phi_{11}$ ...
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1answer
30 views

Error Calculating MVN Likelihood of Time Series with AR(1) Errors in R

I'm having trouble calculating the likelihood of a time series with AR(1) errors. I am generating my covariance matrix according to page 2 of (http://cran.r-project.org/doc/contri...regression.pdf), ...
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Estimation and model selectionfor Gaussian AR(p): conditional and unconditional log likelihood

Suppose a Gaussian stationary AR(p) process is $$ X_t = \sum_{i=1}^t \phi_i X_{t-i} + a_t $$ where $a_t$ is from iid $N(0, \sigma_a^2)$. For estimating its parameters from a sample path of length ...
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1answer
28 views

How to take into consideration gaps in time series?

I've been analysing what is the probability of that measurement going up or down during a week (e.g. 4 times out of 7, I have 60% chances of my measurement going up) everyday for the last 100 days, ...
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66 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: ...
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12 views

Least-squares, pivoting/rotating around a reference point?

Suppose I have two datasets $y1$ and $y2$ sampled equally on $x$. I would like to bring $y2$ in register with $y1$ in a least-squares sense, and referenced to one point at $x_{ref}$ in $y1$ (i.e. the ...
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63 views

Time series modeling with R on weekly data

I am trying to do time series modeling and forecasting using R based on weekly data like below - ...
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1answer
21 views

Regression with TBATS error?

I'm working on a time series model which includes multiple seasonal components (daily and weekly). I believe the best way to approach this would be BATS/TBATS model, however I have a concern if I can ...
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1answer
28 views

Lévy stable vs. extreme value distributions

I'm trying to understand the advantages (if any) of employing the Generalized Extreme Value distribution (GEV) vs. a stable distribution in the context of understanding the probability of crossing a ...
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1answer
103 views

How do I interpret regression coefficients with autocorrelated residuals?

I am building a regression model of time series data in R, where my primary interest is the coefficients of the independent variables. The data exhibit strong seasonality with a trend. The model ...
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23 views

Combining ARIMA models

I have $n$ ARIMA(1,1,1) models. $Y(t)= \mu + Y(t-1) + \phi(Y(t-1)-Y(t-2))-\theta\epsilon(t-1)$ They are all trained on different blocks of data for the same univariate time series. Now I get a list ...
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32 views

How would you deseasonalize this data

I am dealing with electricity prices, and they have 3 know "seasons": Prices during the day follow a pattern; Prices in the weekend are lower than during the week; In summer prices are different ...
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Relation between stationarity, MA and AR processes?

So I know a time-series can be either stationary or non-stationary. I also know that non-stationary prevents us from using many of our econometric tools since our variances would be biased. Therefore, ...
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75 views

help on how to include term $\exp(β_t)/(1+\exp(β_t))$ in AR(2) model

I am trying to include a term in an AR(2) model: $$Y_t=\left( a_0+a_1 \frac{\exp(\beta_t)}{1+\exp(\beta_t)}\right)Y_{t-1}+bY_{t-2}+\delta\epsilon_t$$ Can anyone please help me with this? I don't seem ...
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1answer
116 views

Determining the best correlated time series

Before asking, I read similar questions, but none of them lead to satisfying answers for my specific interest. I want to homogenize a climate time series of precipitation of the Dominican Republic ...
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20 views

Predicting a Certain Type of failure and deciding the input time series

I am trying to predict the time to certain type of failure given the following data on Certain Factory Equipments. The data I have are readings collected every day for sensor installed on those ...