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

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Holt-Winters and Abnormal termination in LNSRCH

I try to fit data with Holt-Winters function in R. Nevertheless, i am getting the following message: ...
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23 views

breakpoint analyses on multiple series: how to detect common points

I have 20 time series that span the same period (100 days each), from 4 species sampled at 5 different location. I made a loop to perform a breakpoint analysis on all of them, resulting in 0 to 3 ...
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21 views

How to blend multiple time series models?

I have three different linear, multi-variate time series models with a best fit against the same observed value $Y$ at 1 minute, 3 minutes and 10 minutes horizons respectively. Each model is using ...
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33 views

Hodrick-Prescott derivation in lay terms

I am currently working with the Hodrick-Prescott filter. I would like to understand the equation in lay terms.
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53 views

Spread-Level Plot versus Power Transformation Functions in R

I'm having trouble interpreting the results from the Spread-Level Plot function in R (car package). The documentation says: PowerTransformation spread-stabilizing power transformation, ...
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21 views

How to down weight correlations in my microarray analysis?

Background: I have been tasked in one part of my analysis to reproduce a method used in another study as follows in bullet points form: Microarray data from a number of time points Calculate ...
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46 views

Why we check the residuals of ARIMA model for white Gaussian?

I have problem about the assumptions and model verification of ARIMA models. I know that Gaussian distributed assumption is not necessary for fitting ARIMA models but I wonder why a lot of people ...
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20 views

Autoregressive model with input variables in proc arima procedure

I am currently working on the time series analysis for series Y but I have to use other two variable A and B as an input variable in SAS proc arima procedure. But I am unable to interpret the cross ...
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19 views

How do I combine multiple time series models to create a generalizable predictive model?

I have several time series that are each observations of the same phenomenon, for example: Observation 1: 10, 25, 36, 72, 80, .... Observation 2: 32, 46, 78, 90, 100, .... Observation 3: ...
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33 views

Cosinor analysis with repeated cycles

I'm interested in developing a model for the circadian rhythm of hormone levels via a cosinor analysis. I just started looking into cosinor analyses so I have a few questions. The data is being ...
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45 views

How to stationarize profit and loss data with an increasing variance and large negative values for time series analysis?

PnL can take large negative values, and its variance increases over time as the firm grows. If we do differencing, an increasing variance remains. If we take log, negative values cannot be defined. ...
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16 views

How to use a set attributes of an entity at different time snaps to make predictive analysis?

The problem is to come up with a classifier for any task based on a set of attributes of an entity having different values at different times. For instance think about football players and their match ...
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40 views

how to calculate a summary value and statistical error in time series

I have a set of data that comes for empirical measurements over a number of days. From the beginning of the experiment to the end of it, every five minutes temperature was measured inside (Ti) and ...
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13 views

estimating period and dealing with Non negative values in forecasting

When I read time series in a ts object and put a period: 1) tr <- ts(data[,4],frequency=). This works for two different periods and decomposes perfectly to show (downward) trend, seasonality and ...
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21 views

Wilcoxon rank sum test for significant differences between two time points

I have data like so …(Col1:Companyname Col2:Data at timepointA; Col3:replicate Data at timepointA; Col4:Data at timepointB; Col5:replicate Data at time point B ...
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10 views

Count variable as control variable in regression in SPSS

I'm doing a research on development of audit fees in 2005-2012. I'd like to see if there's a downward or upward trend in them. I have made a count variable of the years (2005=1 2012=8) and now should ...
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38 views

A Kalman Filter for estimating z-scores?

I have been struggling to fit the following problem into a linear state space model for a Kalman Filter (KF). I'm having a hard time seeing what I'm doing wrong. I suspect I'm violating some law of KF ...
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77 views

Time series - correlation and lag time

I am studying the correlation between a set of input variables and a response variable, price. These are all in time series. 1) Is it necessary that I smooth out the curve where the input variable is ...
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17 views

How to calculate confidence interval for difference (or ratio) of two different time series?

I have two time series which are sampled at the exact same times. I would like to calculate a confidence interval either for the ratio between the two or the difference between the two. The values ...
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21 views

Interpret transformations in Granger causality analysis

I have two time-series with positive counts which roughly look like http://www.google.com/trends/explore#q=italy . I'm new to time series analysis, so I used ...
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2answers
86 views

Which forecasting method for load profiles

I'm new to this forum and I'm quite new to forecasting. Currently I'm trying to learn the basics about exponential smoothing, ARIMA etc. Now I want to forecast the total energy consumption of a rather ...
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3answers
109 views

Library routine for rolling window lag 1 autocorrelation?

I am looking for a library routine that will calculate the lag 1 autocorrelation of a time series with a rolling window; meaning "slide a window of size N points along the time series, calculate the ...
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38 views

Non-seasonal periodicity in Time Series residuals

I have been working on a forecast model in Excel extrapolating from a small (150 data points) monthly time series. I've converted into a year/year percentage change series to get it stationary, ...
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51 views

Goodness of fit (Kolmogorov-Smirnov) test for a time series?

This is a somewhat basic question, I guess. I have a sequence of random variables $X_1,\ldots,X_n$ that I believe to be i.i.d. uniform on $[0,1]$. Being i.i.d. uniform corresponds to my model ...
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37 views

Which type of random walk (1? 2? 3?) can unit root test?

Campbell (1997) showed that there are three types of random walk hypotheses classified by the level of restrictions on $\epsilon_t$: RW1 (random walk 1: increments are independent identically ...
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proportions at two times with predictor

I wonder if anyone can help me. My data has 3 main variables: proportions at 2 time periods, and an additional predictor. For example: Item Type Y(t) X Y(t+1) 1 .05 ...
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Comparing anomaly detection algorithms

My question is how can different anomaly detection algorithms be compared for my specific dataset. Essentially, I have multivariate timeseries (physical quantities such as temperature, pressure) and a ...
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23 views

time-series analysis Vs statistical signal processing

Is there a way to identify when to use time series analysis or signal processing. Time series data analysis can be divided to signal processing and normal time series analysis. In signal processing ...
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29 views

How to get the baseline measurement in time series data?

I have a time series data of bike's rentals in which each row represents a specific hour and a number of bikes rented in that specific hour. The task is to predict the rentals for the last 10 days (of ...
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128 views

How to form a predictive model in R?

I have two data sets whose structure is like this: DATA SET 1: ...
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68 views

Learning to map vectors to vectors

Say we want to learn a function: $f(\mathbf{x} \in \mathbf{R}^p) \rightarrow \mathbf{y} \in \mathbf{R}^q$ where $\mathbf{x}$ and $\mathbf{y}$ are vectors representing time series. We have multiple ...
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60 views

How to develop a prediction model based on correlation in R?

I have two sets of data, say sales and profit, and I have calculated the correlation between these two data over different months using R. So currently I have ...
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25 views

How to isolate impact of event in a product's lifecycle?

I'm trying to figure out how a single event affects sales numbers of a song. For example, see what the effect of being featured in iTunes store compared to songs with comparable previous download ...
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70 views

Statistical methods for analysing correlation between stock price index and natural disasters

Can someone help me identify what statistical method (or any method) that I can use to correlate the effects of natural disasters on Stock Market Index?
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37 views

How to give an input when you are using Machine Learning method in R

I am new to R and machine learning algorithms. I have basic knowledge of different machine learning algorithms. I have four years of daily sales data.I am trying to predict sales using Support Vector ...
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199 views

Is there a convenient form for this large covariance matrix?

Consider the following bivariate vector autoregression: $$X_t=\mu +X_{t-1}A+\varepsilon_t,\ \varepsilon_t \overset{iid}{\sim} MVN(0, V),\ X_t=(X_{1,t},X_{2,t})',$$ where the assumptions on the ...
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26 views

Inference from a sometimes-random time-series

Let's say we have two cointegrated time-series, $Y_{1}$ and $Y_{2}$, and I want to assess the causal impact of $Y_{1}$ on $Y_{2}$. There is good reason to think that both variables are influenced by a ...
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27 views

Techniques to forecast discrete events in a time series?

I'm currently looking at time series data for patients who have been admitted to a hospital. The time series itself models risk probabilities, where high risks are marked by peaks. At various points ...
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67 views

Forecast with predictable market events

I'm trying to build a predictive model to forecast the residual value of used electronic equipment. As a first step, I created a few quick plots in order to visually identify any interesting features. ...
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55 views

Temporal abstraction in Churn analysis: Why do we need it?

Could you explain the need of temporal abstraction in churn analysis intuitively with a simple example? I tried Google but there are not any clear answers , especially for churn analysis.
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Applying ARMAX model from r output

I'm trying to apply R output to generate a scenario using external data, I'm not sure how exactly to use the coefficients in each from the R output. I have an ARMAX(1, 1) model Coefficient of AR1: ...
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21 views

Prediction using CRFs for time series data

I have a little confusion about validity of some predictions I am making using a CRF model I have trained. The CRF model is trained on some input time-series, and when making predictions, I am ...
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40 views

VAR and Granger causality test

Is it necessary to calculate VAR before Granger causality test so that we can have the lag length to be used in Granger causality test
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27 views

What kind of analysis gives you the statement "If you DONT reach X amount by time T, then your chances go down by P percentage?

I am trying to model growth for data I have regarding downloads of applications. I would like to make a statement, if you "DONT reach X amount of downloads by time T, then your chances of reaching 15 ...
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29 views

A question on no. of training examples and decision trees

I have a set of around 200,000 training instances. Each training instance consists of an attribute called $duration$, a discrete integer type and a time series of floating-point values, in form of a ...
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54 views

Forward Filtering Backwards Sampling (FFBS) and Look-Ahead Bias

Assumptions / Context: Let's assume that I have data that can be modeled as a dynamic linear model. To estimate the parameters (e.g., covariance matrix of the state/system equation), I use a Gibbs ...
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10 views

Signal dimension in regression model

Estimating Unknown Sparsity in Compressed Sensing is a paper about sparse signal. I am just learning the concepts. In the first paragraph, it says that when the number of observation data samples $n$ ...
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18 views

What is the procedure to compare two different period time series

I am currently working on the task that I would like to compare two different period time series like Sales in 2012 vs Sales in 2013. Kindly suggest me any statistical procedure.
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What is sparse regression model

I am learning the concepts of Sparse regression and facing initial hurdles in terminology. sparse regression model explains the definition of what is meant by sparse. When the number of samples $n$ ...
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12 views

How to model occurrence counts in groups with latent membership?

I have a dataset that describes the daily history of occurrences of a certain phenomenon P among a certain population. (These are subdivided into certain classes or forms that the phenomenon can take: ...