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

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Multivariate binary time series

I have several concurrent time-series, which have binary response: Yi = (yi1, ... , yiT) where yit = 1 or 0 at an observed time t. i = 1, ...,n (where n is the total number of concurrent time ...
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23 views

Why can't my (auto.)arima-model forecast my time series?

For testing I generated a very simple time series with a clear recurring pattern. I expected that auto.arima will generate a model, that can forecast that pattern, but óbviously it doesn't. Can anyone ...
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19 views

Nonlinear forecasting

I'm working with time series data (which fluctuates constantly) and currently have 27 data points to forecast with. Would anyone be able to recommend a nonlinear forecasting method using formulas to ...
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1answer
33 views

Am I causing statistical violations? [on hold]

I am trying to analyze where the significant differences are between 2 sets of time series. Group 1 (Expert) has 29 trials normalised to 256 points whereas Group 2 (Novice) has 19 trials (see attached ...
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18 views

Why can't we use top-down methods in forecasting grouped time series?

As I asked in here I was trying to forecast grouped time series with two grouping variables and I find some limitation of hierarchical forecasting methods. In particular, using hts package from R, we ...
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21 views

How to use Singular Value Decomposition for time series?

I want to represent a time series using the SVD algorithm. Below are some representations from this presentation. The SVD representations is formed by summing k "eigenwaves" corresponding to the ...
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6 views

Understanding changes in bookings per medical practice

I have data for counts of bookings per day. I have data for counts of active medical pracitces per day (active means that they have published appointments that are able to be booked in the past 28 ...
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14 views

Constant in arima model whether to include or exclude?

I have a very basic question on including constant in Arima models. I'll illustrate this by an example. I have the following ACF and PACF of a weekly time series that is differenced at lag 1 (trend) ...
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20 views

Work with results of tbats decomposition

I made a time series decomposition with tbats. There is weekly and yearly seasonality in the data (and maybe also monthly - not really important for the question) ...
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22 views

Online time series forecasting with DLM

I have estimated a univariate time series model, consisting of a random walk and an AR component. Now the goal is to make forecast about a couple of steps ahead as new data comes in, in an online ...
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16 views

Cox Time Series Data — Analysis of Interaction Terms

In a time series data set using Cox Proporational Hazard Rate, I am testing a model with interaction terms. I am worried that my interaction term is biased by several specifications of my model and I ...
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33 views

Recommend e-book that is comparable to Hamilton's Time Series Analysis?

(NOTE: I have read the topic re "books for self-studying time series analysis," this question is intended to be different in a very specific way, and I am looking for answers that would not be ...
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simple exponential smoothing - Ljung-Box test - residual

I'm a newbie in statistics and actually I'm studying Time Series. Reading this page (http://a-little-book-of-r-for-time-series.readthedocs.org/en/latest/src/timeseries.html) I found this sentence: ...
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Best forecast method for my data

I have a large amount of statistical data on tennis matches over the last 10 years and want to be able to forecast the percentage of points a server will win on his own serve based on past data. For ...
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1answer
31 views

Benchmarking time series forecasting model

Problem: I'm building a time series forecasting model for daily data wherein, the aim is to forecast for the next one week. So, to validate the model, I'm using a moving window based validation ...
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16 views

DLM, regression and multiple time series

I'm working with incoming traffic data at multiple spots along a long road. Let's denote the traffic at time t at point $j$ by $x_j(t)$. For each spot, a univariate model, such as local level plus ...
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2answers
58 views

Assigning Weights to An Averaged Forecast

So I've been learning how to forecast over this summer and I've been using Rob Hyndman's book Forecasting: principles and practice. I've been using R, but my questions aren't about code. For the ...
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13 views

Compare several binary time series

What is the best method to compare several time series taking into account not only the overall number of overlapping points (as Hamming distance does), but also to catch somehow the fact that the ...
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ARIMA possible with multiple groups?

I’m looking to build an ARIMA model in R to help me predict the number of shots a football player is going to take in a game. I have last season's data to analyse to determine the optimal lags for ...
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2answers
34 views

Time-series for website traffice analysis aftere adding influencer?

On my website (interactivia.ro) I've added a gamification module from CaptainUp. I'm interested to find out how this gamification module influenced my website traffic. The data extracted from Google ...
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Computing fit of model to horizontally-misalligned time-series data

I have a model that predicts the level of harmonic tension in a piece of music, at every note/chord in the piece. I also have participant data (time series) that contains subjective ratings of tension ...
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10 views

Testing Proportions of People Over Time

Let's say we have two time points (2013 and 2014 for sake of argument). In 2013, 50% of customers who come to my shop buy milk (100 out of 200 customers). In 2014, 75% of customers who come to my ...
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25 views

How to analyse transfer function using R? [on hold]

I want to learn about using transfer function time series in R. But I don't know how to do it. Anyone know how to do it? Anyone know some references about using transfer function using R?
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Do we have to model spurious auto-correlation in time series?

I am analyzing a data set of power consumption with the aim of forecasting. The times when there is consumption are rather sparse. If there is consumption then there is likely one in the next time ...
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Continue the predictions beyond the current data using time series neural network

I have a single time series variable and I want to train a neural network in a sort of auto-regressive fashion. specifically, I have data for water consumption that is changing with time In the ...
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7 views

Mixed effects model, pseudoreplication in space, change through time

I have not found a good example for data with my structure. The data come from a long-term observational study. The response variable is growth rate, with one measurement from an individual fish. ...
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2answers
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CausalImpact - Should I use more than one control?

In the intro document (https://google.github.io/CausalImpact/CausalImpact.html) it suggests that using one predictor is not ideal. Am I current in understand that they mean one control? If so, should ...
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Extrapolate Multiple Time-series to Null

Given at least two time-series each associated with an index (that correlates with the differences between time-series), is there an established method to extrapolate a new time-series based on the ...
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23 views

Appropriate predictive model for two random time series with serial correlation

Say I have annual observations of the temperatures at the North Pole and South Pole for many years. I want to build a model that given the South Pole temperature for the current year and all prior ...
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12 views

How do we understand when a time series must be decomposed or normalized?

Why do we use decomposition in time series? How much information will be lost if we will delete (decompose) the seasonal component? Where I can find some documentation which describe what time series ...
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19 views

Comparing similarities and differences of time series data

how to compare the shape of two time series data. E.g. comparing fluctuations of two time series data. like, how to quantify whether one is more fluctuating than the other?
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Option daily “Open Interest” data used for liquidity study(comparison on conversion from american to european style option).

Can somwbody suggest if I can use paired sample t-test in studying the daily average open interest data for 15 stocks using paired sample t-test. I have checked the normality with respect to the ...
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21 views

Granger Causality and Regression

I have an enquiry regarding the Granger Causality analysis. It is said that it is performed to check whether “X causes Y”, or to put it differently, whether X contains any predictive information with ...
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Gap-filling biophysical sensor time series

I am exploring imputation methods for filling gaps in time series from multiple co-located biophysical sensors. At a given site, we have about 25 sensors measuring things like temperature, humidity, ...
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Two-period lagged effect on dependent variable due to increase in independent variable

The following time-series is given: yt = 53 + 0.4xt + 0.2xt-1 + 0.1xt-2 + 0.8yt-1 where t denotes the time period. If the is a one-time increase in x with 100 in t, what is the effect on y in period ...
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Calculating probability of sale from auction data

I have some data representing the last 6 months of closed auction data from a particular website. The data I have includes market value of product, actual sale amount, and date sold. I have about 600 ...
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1answer
38 views

How to identify the seasonality of a timeseries from the Periodogram?

I need to identify seasonality/ periodicity of a dataset so as to develop an ARMAX model. This is what the original time-series looks like I have plotted the periodogram of the dataset. Ps: I used ...
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43 views

Train neural network for forecasting

I am trying to use time series neural network to predict future values. I have time series data from 2010-2014 and I need to predict the values from 2015-2020 using time series neural network. I am ...
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26 views

Unit root test results not stationary, can I apply VAR?

I am working on my project methodology, and I am planning to use VAR model. In order to proceed with VAR, I run my data thru unit root test in Stata, and found that my data is not stationary. Can I ...
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21 views

PMML and time-series

What is the purpose for embedding the original and predicted time series in PMML models (http://www.dmg.org/v4-1/TimeSeriesModel.html)? I don't understand how these embedded series are being used by ...
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How can I intepret and compare ARIMAs from different data sets?

I have human chromosome data. I have 23 chromosomes that consist of equally spaced windows of 100,000 base pairs with a dependent variable attached. I am treating this like a 1 dimensional spatial ...
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Newey-West vs Cochrane-Orcutt

I have time series of 189 observations and I want to regress $y$ on $x$. My modeling procedure is the following: I run an OLS and I get the constant significant and b not significant (but I know ...
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How to include seasonality in the data ARMAX model that has multiple periodicities?

I am doing regression with ARIMA errors. The residuals are as shown in the figure below. Clearly, the scatter plot shows that this time series has some sort of periodicity or seasonality, but its very ...
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26 views

comparing ARIMA and AR with external regressor

Consider the following models fitted to the same time series: ARIMA(0,1,1) ARIMA(1,0,0) (that is, AR(1)) with an external regressor Can I use the AIC (or any other information criteria) to decide ...
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33 views

ARIMA modeling white noise probabilities vs. residual autocorrelation/PACF

I have moderate understanding of statistics and time series analysis. I trying to forecast a weekly time series with lots of outliers and trend shifts. After correcting all of the outliers, I'm left ...
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Measure changes between the first and last point in a series of data

I'm unable to figure out what the common practice is for measuring changes across a selection of points in a time series, not just the difference of the first and last points. For instance, let us ...
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12 views

Time series forecasting using ANN

I have an array of data recorded from vibration analysis of a bearing.I want to know how to forecast 30 day later. I don't know machine learning and I'm not so familiar with neural network for example ...
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28 views

Modeling time series binary data in R

I have time series binary data with other explanatory variables (qualitative and quantitative). I would like to see pattern of events over the time, and influences of different variables using R. I am ...
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Clarification in the differences between several time-series analysis models

Can anybody give me a simple explanation of the differences among the following: ARIMAX model Regression with ARIMA errors Transfer function model Please provide some references if you can.
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How to interpret the following Baysian time series representation (picture attached)

I am trying to understand this paper on Bayesian Hierarchial model (http://www.umac.mo/fba/irer/papers/past/vol13n1_pdf/01.pdf) in which one of the sub-models is a time series with random-walk ...