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

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Statistical test for increasing incidence of a rare event

I have following simulated data of 2500 persons regarding the incidence of a rare disease over 20 years ...
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Poisson vs. Gaussian in Geomagnetic Data

I've been studying geomagnetic signals using a threshold approach to detect pulse events in the data. The question here is what is the significance of the crossover of stddev and mean as the ...
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Why does the variance of the Random walk increase?

So the random walk that is defined as $Y_{t} = Y_{t-1} + e_t$, where $e_t$ is white noise. This denotes that the current position is the sum of the previous position + an error term. You can prove ...
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Coherence vs. Magnitude Squared Coherence

Currently I am writing my master thesis. The theory part is about the turbulent wind field generation, where the coherence (not magnitude squared) is used: $$\text{coh}(f) = ...
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Mixed effects model for repeated measures to test for factors that are either constant or dynamic within an individual over time

I am dealing with a rather complicated dataset with repeated measures of the same individuals at various time points (samples were collected at different time points and different number of samples ...
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Simulation of time series with R [on hold]

I'm new on time series. I'm trying to solve an exercise on the simulation of an ARMA process. The problem is the following: Generate 100 simulations, each with n=60 elements of an ARMA(1,2) process ...
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Package ‘JPSurv’ - Trends in incidence rates [on hold]

Has anyone ever used the JPSurv package in R to look at trends in incidence rates? The original software put out by the NIH - Joinpoint - is not compatible with my computer (a macintosh) and so I ...
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How to test whether a random walk have a upward or downward trend

I have applied Kalman Filtering Method to get the estimates for time-varying coefficients (using DLM package in R). The model is like $S_{t} = \alpha_t * A_t + \beta_t * W_t + \gamma_t * A_t * W_t + ...
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non-invertible weakly stationary AR processes

ok, so I know (for example) that $X_t = 2X_{t-1} + e_t$ for iid $e_t$ is stationary. But how do I go about proving the condition $Cov(X_r, X_s) = Cov(X_{r+t}, X_{s+t})$ for all integer $r, s, t$ ...
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Detrending a series that moves about zero

How can I avoid my de-trending model from blowing up? Do I need an additive model rather than a multiplicative model? If so, is there anything I would consequently need to take into account? Can I ...
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Can I run an SVM on sparse temporal data without a regular time interval?

I have data of occurrences with timestamps that could be days or months apart. I'd like to enter the values natively as follows. Are there any SVM algorithms that can support such an input? day 1: ...
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Help¡ How do I solve this problem time series? Subject: Geophysical time series [on hold]

A problem of interest in the analysis of geophysical time series, involves a simple model for observed data containing a signal and a reflected version of the signal with unknown amplification ...
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How can i predict data just for specific days in R? [on hold]

I would like to know how to estimate data in specific period of time in R. for example, in my problem i should estimate data from 26th October 2005 to 31st October 2005 in monitoring station and I ...
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Evaluating correlation of 2 time series

I did some research on this website and I found a few questions about correlation between 2 time series. However, all of them end-up with cross-correlation as answer, which is not really what I want. ...
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Vector Autoregressive Model: residual's kurtosis proportional to number of lags?

I have some transformed data set (windspeeds that are nearly-weibull-distributed). I transformed this data which results in near-normal distribution (close to no excess kurtosis and skewness of zero). ...
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How to choose which test to use to determine if a time series is non stationary?

I want to determine if my time series is non stationary. I looked many articles and have learned there is not just one formula - ADF. It seems you must use some series of tests and assumptions. But I ...
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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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How to detect variable seasonality pattern

We have predicted and actual (daily) data for past 3 years. We use 90 days of data for prediction. Generally our predictions are very accurate, but we receive unusual traffic for few days/weeks ( like ...
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Identification of navigation pattern (lapping, pacing, random and direct) from X,Y co-ordinate in known physical layout

I have X, Y and Z co-ordinate of the movement patterns of a person for 30 days over some known physical layout. This is unevenly spaced time-series data with maximum frequency of 2Hz while in motion. ...
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Difference between clipped time series, PRBS, PN sequence and their application in signal processing

A real valued time series can be converted into binary series through the process of clipping. Clipping, or hard limiting, a time series is transforming a real valued time series Y into a binary ...
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What techniques infer dependencies between time series?

I'm working on an ML project. We have an app where users can track 1) binary variables and 2) quantities over time on a scale from one to ten. For example, at a given time they may track whether they ...
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Prerequisites for time series and ARIMA

I am working on forecasting sales for 2016. The details of the problem is: 2014 - sales happened only between Jan-Apr 2015 - Sales happened only between Jan-Apr The sales in rest 8 months of the ...
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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 ...
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What does the sigma^2 figure mean when fitting a model to a time series in R?

I have a time series in R, and I am using the arima function to fit it to a SARIMA model. I would like to use the parameters it returns to write the time series equation by hand, but in order to do ...
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What is the best way to extract time-series shared by two variables?

I have one dependent and several independent variables. I want to extract the time-series of the independent variable that is shared with the dependent variable. In other words, I want to extract only ...
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Analyze trend given a set of points

Suppose I have a set of N values representing EUR/USD rate... [ 1.10 , 1.20 , 1.25, 1.20, 1.19 ] Which is the simplest way for analyzing if values tend to raise or tend to fall? I'm looking for a ...
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Correlation computing time series data of unequal sample size

I am working on a computing a correlation between two variables X and Y. Both are time series and were recorded during the same period. 6/25/15. However X was recorded much more often than Y. ...
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Which time series model to use?

Hi I have a large data set of objects, each containing a list of the same attributes. The data is arranged in a time series so that the value for an attribute for an object is indexed by its time. ...
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Covariance of ARMA (2,1)

I am preparing for an exam and need help. Consider the following estimated ARMA(2,1) model, y(t) = 0,05 + 0,83y(t-1) + 0,13y(t-2)- 0,15e(t-1) + e(t): Given the unconditional variance and 1st order ...
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Time series analysis with known outliers

Let's say I have a time series of site usage data that I would like to analyze. The data is for people visiting my site, with a known seasonality that I can easily calculate, but in addition, there ...
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Touch times to authenticate user

for a project I gathered touch data of different users when they tap a rhythm repeatedly on the touch screen in a game. ...
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Transfer function of pure ARMA time series model

Is it theoretically justifiable to calculate/use the transfer function of a pure ARMA model? I would like to be able to use the transfer function representation to put the state equations into their ...
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X11 deseasoning: too few datapoint for S3x3 moving average?

Do I have too few data points to use a $S_{3\times3}$ moving average? I have the following dataset for January months, ...
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Marketing Data with many zeros

I am working on a marketing data which is a time series data with marketing spend done through different channels and revenue generated. The data looks like this : My data contains too many zeros ...
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Deciding the lag while testing for timeseries data stationarity

I am currently reading up on time series forecasting using ARIMA in SAS. I just began to go through what has been explained here : ...
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Do we need to detrend when do Cross-Correlation between two time series?

I have a group of time series variables and I want to found out the relationship among them. The method I use is to calculate pair-wise correlation between two time series and found out those with ...
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AR(2) model is causal

AR(2) model is : $$X_t=\phi_1X_{t-1}+\phi_2X_{t-2}+W_t$$ Where $W_t\sim N(o,\sigma^2)$ I want to prove AR(2) model is causal . So , I tried as : $$X_t-\phi_1X_{t-1}-\phi_2X_{t-2}=W_t$$ ...
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Modeling Time-Series with a lower bound

I am trying to fit a model to a time series that has a lower bound (at around -150). Using an ARIMA model, running simulations often leads to the time series hitting (and going underneath) this lower ...
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Linear correlation between two sets of data

I have various data sets that correspond to values (in percentage) at given time points (hours, up to 10 hours). The total number of data sets of values at given time points is 8. My question is ...
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Getting an error message “Error in if(reached.threshold < min.reached.threshold)…” while training network using neuralnet package

I'm using R to create train and test a neural network on a time series (the annual sales of a company over a large period of time). As using the package's default learning algorithm (resilient ...
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35 views

Dealing with nonstationarity and autocorrelation

Relationship between interest rates and retail sales. I have a time series sample of quarterly data for 10 years. My dependent variable is retail prices and independent variables are interest rates, ...
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Sampling Distribution of Sample Correlation Coefficient

For a linear process $X_t=\mu+\sum_j\varphi_jW_{t-j}$ where $W_t$ is white noise and $\mathbb E(W_t^4)<\infty$ , $$ \begin{pmatrix} \hat\rho(1) \\ \hat\rho(2) \\ \vdots \\ ...
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Hodrick-Prescott Filter, Time Series, SDE, and Ito Isometry

The background of this question is a paper written by Morten O.Ravn and Harald Uhlig, titled "On Adjusting The Hodrick-Prescott Filter For The Frequency of Observations" Consider the decomposition of ...
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R: Time series forecasting alternatives to ARIMA [on hold]

I was using WEKA to perform time series forecasting based on lagged variables and machine learning algorithms: Time Series Analysis and Forecasting with Weka I am trying now to do something similar ...
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Forecasting with Dynamic values using R [migrated]

I have a Json object : {"tcDetails":[{"project_nm":"abc","id":"1","n_tc":"32","TC": [{"29/06/2015":50,"30/06/2015":45,.....}] {"level":[{80,85,90,95}]}]} ...
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Eviews and Forecasting Linear Regression with AR(1) Error Term

This question is geared towards those who are familiar with Eviews and forecasting with linear regression in the case of AR(1) error terms. Consider the classical linear regression model where the ...
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EWMA or Moving Average when Estimating Trend in Seasonal Data

What is generally the best practice when estimating trend (non-seasonal component) in seasonal data? Centred Moving Average as suggested by MatLab docs Averaged EWMA (backwards & forwards) as ...
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Making a Time-series Stationary: Order of Operations

Following various sources including this post, which is the correct approach to making a time-series stationary? Remove trend Remove seasonality or Remove seasonality with estimated trend ...
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Comparing Variance Ratio tests to Hurst exponents

I have used the Chow Denning test and the Hurst exponent (Peng, Whittle and R/S methods) to examine if a particular time series follows a random walk. My results are conflicting between the 2 tests. ...
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Which is the best way to check (with some certainty) if the mean of a time series is a constant?

I am testing the time series output by a light sensor, and trying to know when dawn and dusk end. I used a cusum test to check when light level starts changing, and now I need to know when it ...