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

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Markov Chain State Transition Probability in R

I have a dataset which shows the states (3 states) across 11 time points for each participant. I wanted to estimate the Markov Chain state transition probability matrices for time points 2-11 using R. ...
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2answers
371 views

Kalman filter transition matrix

Hi guys I am trying to writ e a code on python to correct forecast data using Kalman Filter. I am following the equations and recommendations in this link : ...
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10 views

8 variables for 12 months. Sigmaplot

I have measeured 8 variables for 12 months. n = 5-20. So, now I have mean, STDEV, SEM and n for those variables. I have trying to show relationship within those variables and among months. So, I ...
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16 views

a question on time series [on hold]

I'm studying with time series. Please tell me whether there exist trend for each plot. I think that there exist. But I want to check it. ,thank you
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2answers
965 views

How to approach time series regression with monthly dependent variable and quarterly independent variables

I am building a regression model where my goal is to obtain a monthly forecast of the dependent variable for the next 2 years. I have a monthly historical series available. For my independent ...
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14 views

Using VAR in R to estimate the effect of oil prices on stock returns [on hold]

I am having some problems with estimating a VAR in R. I am trying to replicate a study from Park and Ratti 2008 Using a time period from January 1997 to February 2016, I have been able to perform ...
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1answer
484 views

$ARIMA(p,d,q)+X_t$, Simulation over Forecasting period

I have time series data and I used an $ARIMA(p,d,q)+X_t$ as the model to fit the data. The $X_t$ is an indicator random variable that is either 0 (when I don’t see a rare event) or 1 (when I see the ...
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9 views

Multinomial logit / time-series fixed effects / multivariate regression: Which one to use in this case?

Friends, As part of a larger study, we have collected a wealth of data on the interactions customers engage in when buying and using a service. Particularly, we have distinguished this process into ...
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5 views

Statistical test between pairs of values recorded by human and machine inorder to find the agreement between them

I have a set of independent values (which are subjective) recorded based on both human observation and machine recorded ones. At this point I want to ask serval questions like what is the agreement ...
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1answer
305 views

Using a Lag Variable in Time Series Data

I am new to Time Series Data and this question is confusing me, as I have received different advice and was wondering if I could request clarification. I am attempting to test whether the creation of ...
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4 views

forecasting with ratio

i have daily data about revenue and number of push notification sends. I am trying to predict revenue/sends by day. there is a day of week effect also and days may have different sends. For example ...
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1answer
169 views

Time series with correlated observations: How to start analysis?

We have a time series dataset: Daily arrivals of asylum seekers. Goal is to model this variable. In particular we would like to attempt Arima modeling and/or fitting a distribution. Before we get to ...
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1answer
192 views

Optimal lag order selection for a GARCH model

My research is forecasting petrol demand. I want to fit a GARCH model. I am using a sample of 260 weekly observations. My data set has only one variable. Is there a method to find the optimal lag ...
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2answers
32 views

R auto.arima with intervention: intervention only affects one point

I have a model fitted with auto.arima, the model is ARIMA(0,1,0)x(0,1,0)[6] with seasonal period 6. The data is bi-monthly so there is an annual seasonality. There ...
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0answers
6 views

Do I still need ergodicity if I have multiple/infinite time series of the same data generating process?

The main reason we need ergodicity (and therefore stationarity) is, as Shalizi puts it: The ergodic theorem is important, because it tells us that a single long time series becomes ...
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18 views

Optim error training ARIMA model in R [duplicate]

I have the code below which trains ARIMA models for a range of order combinations. I'm getting the error below in the step training the ARIMA models. The code worked just fine with the ...
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1answer
267 views

What is an appropriate statistical test to identify significantly different time-points in two time-courses?

I have two time-courses. Both are the same length. Both are univariate. Each represents the average EEG signal from a unique subgroup. The two subgroups do not have the same number of subjects. I ...
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2answers
142 views

Forecasting a time series based on a collection of other time series

I am new to Time Series analysis, but I read a lot of questions here which deal with how you can forecast values of a time series based on its history alone. Now, the data I have is of this kind : ...
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1answer
277 views

Comparing two sets of data over time to infer correlation or imply causation

I have two data sets, over a period of time that I would like to compare. I am very unfamiliar with statistics so sorry if this is simple. I need to use SPSS. I am comparing the number of journal ...
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19 views

MAPE vs. MAE for forecast evaluation [duplicate]

If you are trying to judge how well a forecasted model is doing, say like the rolling forecast example from Hyndman's blog, is MAPE a better choice than MAE? Are there reasons to chose mape or mae ...
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14 views

Estimating the distribution of the mean of a sum of AR time-series

So my problem is this: I am trying to model a population based on a sum of populations. For example, lets say this was the United States Population. I have data for the last 5 years about populations ...
3
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1answer
184 views

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}\\ ...
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1answer
752 views

How can I recreate a Weibull distribution given mean and standard deviation and the shape and scale parameters are unknown?

Figure 2 is a Weibull distribution of three different wind farms in Canada. These 3 probability distributions were combined in a study to obtain a common wind speed model. I will be using this common ...
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24 views

Filtering sudden peaks with its rolling mean

I have a 10minutes wind speed time series which has several high/low sudden peaks that are out of the general tendency of the series and its sorrounding neighbors. While searching for an automatic ...
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6 views

Converting time series into vector [migrated]

I am looking for an approach to convert a time series data into vectors. An example of what I am trying to achieve is given below. ...
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15 views

Showing that a process is autoregressive

Consider a sequence $(Z_t)$ of i.i.d. standard normal variables and real numbers $\alpha > 0$ and $\theta \in (0,\frac{1}{3\sqrt{3}})$. Let $X_t = \sigma_tZ_t$ for $\sigma_t^2$ defined by ...
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9 views

Linear transform of a strictly stationary time series

First, let me clarify what I mean by a strictly stationary time series. Let $(X_t)_{t\in \mathbb{Z}}$ be a sequence of random variables on some probability space. If it holds that $$(X_t, ...
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18 views

Optimal block length for block bootstrap with multivariate time series

I've got a multivariate time series $\mathbf{X}_t$, where $t$ is time and there are $p>1$ columns of $\mathbf{X}_t$. There is autocorrelation in the data. I'm interested in various functions of ...
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1answer
199 views

Creating a composite rank of restaurants with some missing data

I have monthly sales data of 500 restaurants for one year. For the same restaurants, I also have customer defection or dissatisfaction rates. I want to create a composite score that can rank ...
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10 views

Analyzing series of events, controlling lengths

(Excuse me for terminological problems. After I tried to find the solution, I started looking for at least the right names for the concepts I use, but I failed, as the simple descriptions I tried to ...
2
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1answer
24 views

Predictions from BSTS model (in R) are failing completely

After reading this blog post about Bayesian structural time series models, I wanted to look at implementing this in the context of a problem I'd previously used ARIMA for. I have some data with some ...
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1answer
11 views

DCC-GARCH: selection of error distribution and extraction of volatility decay

I am in a hesitation of detecting which indicators from maximum likelihood (ML) estimates of the Gaussian DCC model tell the volatility parameters' decaying. Another question is, how to know which ...
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8 views

MODWT Wavelet Transform Matrix

I am working through the calculations for an MODWT transform. In order to visualise it, could someone show me what say the $W_1$ 8x8 matrix looks like. For the first level of a d4 wavelet, I have: ...
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1answer
11 views

Estimate the paramters of the simplest multiplicative error model

I am trying to implement the simplest multiplicative error model possible, to understand how it works. MEMs are time series model (introduced by Robert Engle), where instead of the components being ...
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1answer
180 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, ...
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13 views

How to detect a relatively small level shift(leakage) in an hourly water flux time series in an area?

Background I'm working on a project which aims to use the history data about a water flux to detect whether there is a leakage happened. The data is hourly collected and among about 4 months. I've ...
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1answer
33 views

Fitting Methods in Arima

Could someone please explain the differences between the 3 fitting methods, method = c("CSS-ML", "ML", "CSS"), in Arima? If I run the code below I get an error message, but if I specify method="ML" ...
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1answer
19 views

Performance of Logistic Regression with time

I am building a predictive model using logistic regression to predict if an applicant should be given a credit product based on their telecommunication data of the previous eight months postpaid ...
3
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0answers
30 views

Lack of independence - how to analyze data from a time-series that are spatially correlated?

I am dealing with a species distribution. From aerial imagery, the species' presence has been assessed at specific locations by identifying the presence/absence of the species of interest under 9500 ...
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29 views

ARMA when ARIMA should be used

(Note: I am taking a first course in time series -- correct me where I am wrong.) What happens when we fit an ARMA model to a time series when a differenced model (ARIMA with nontrivial $d$), should ...
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1answer
36 views

Choosing the maximum lag length in the augmented Dickey-Fuller test

I have a question regarding how to choose the maximum lag length in the augmented Dickey-Fuller test using the "urca" package in R. I want to perform the ADF test on the daily price of a stock index ...
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12 views

How to control serially correlated independent variables?

I'm interested in studying the impact of one variable (e.g., R&D expense at year T) on future firm performance (e.g., Sales in year T+5), I know it's incorrect to specify the following model: ...
3
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2answers
281 views

Normalized RMSE

I have several time-series in a VAR(1) and, due to some of them haven't the same unit of measure, I'd like to estimate the RMSE in percentage. I know that it could be done in several ways (see below) ...
3
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2answers
244 views

ARIMAX model's exogenous components?

Does anyone know, considering an ARIMAX model that fitting a stationary process Y, then do the exogenous components for the model need to be (weakly) stationary? I think exogenous components can be ...
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1answer
17 views

Change from baseline mixed effects models

I am trying to analyze a study that has three treatment groups and the measurements are conducted on the same subjects over time. The first time point is a baseline measurement and then there are 7 ...
2
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1answer
151 views

Correlation or Causality between two time series considering a sliding window

I would perform a correlation and causality analysis between two time series considering only a little window of samples. In this way I would try to find if there is a correlation or a causality ...
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6 views

Weekly Time Series Detrend

I have weekly time series data that has a unit root problem. When i include a trend (in DF), the unit root is gone. This is 10 years worth of data, with gaps, and there are nearly 400 weeks. My idea ...
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585 views

A regression model whose response variable is the day of year that an annual event (usually) occurs

In this particular case I'm referring to the day on which a lake freezes. This "ice-on" date only occurs once a year, but sometimes it doesn't occur at all (if the winter is warm). So on one year the ...
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1answer
148 views

How could I use VAR model for nonstationary series?

I have five independent variables: oil (stationary at level), f (stationary at level), k ...
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7 views

How are datasets labelled for SVM classifier testing?

I am working on a time-series of stock prices, and want to try an SVM classifier based on technical analysis indicators (such as macd, rsi etc.) to predict whether the market situation is bullish or ...