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

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Creating a Volatility Index [on hold]

Edit: Is there any way to create an index that captures volatility in a time series? I'm looking at a simple way in excel preferably. I am specifically trying to create a volatility index of the ...
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ARIMA versus a Mixed model for trend detection

I am trying to find any evidence of warming in monthly times series data of water temperature over a 21-year period that is serially correlated. Essentially I am looking to determine a global trend, ...
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23 views

Time Series Data and SAS

I have a time series data set with 54 observations. I need to use the SAS software. I am aware that I can create a dataset in the SAS library and then open it. however i am not able to open the data ...
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FORECASTING AR(1) Autoregressive Form

Ive been implementing a little exercise to obtain the first 2 forecasting points of an AR(1) process. And i want to have the forecasting ponts using the three forms: Im folowing this pdf ...
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Nonlinear forecasting methods [on hold]

Can anyone recommend a nonlinear forecasting method for time-series performance data? It doesn't depend on seasonality so Holt-Winters isn't appropriate. Edit: the data is arrears percentages for the ...
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Multidimensional dynamic time warping

I am trying to understand how to extend the idea of one dimensional dynamic time warping to the multidimensional case. Lets assume I have a dataset with two dimensions where ...
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Prediction over the time with cohort

I'd like to modelise the evolution of the sales of a store. Here are the date I have : i.stack.imgur.com/6FsZ8.png -customers are aggregated into monthly cohort depending on the date of the first ...
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Need advice on unbalanced time-series dataset, for use with CAPM regression

I have 40 years of monthly historical returns of around 3000 mutual funds. The dataset contains both active and inactive funds, so some funds have data for the whole period, whereas others will have ...
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1answer
24 views

Confusing results on kpss.test() for stationarity

I've got a dataset which clearly shows a trend. However, I want to assess wether this trend is deterministic or stochastic. If I understood it right, I would need to use differences if the trend is ...
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Shock event values in Linear Aggregate Definition of AutoRegressive Process

I am beginner in Time Series and studying (self study) at the derivation of the relation between AR process of Deviations and the Linear Filter process of actual values of Time Series. Have this ...
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Are the data stationary or non-stationary and seasonality?

I want to use Arima model for forecasting wind speed.I plot my data. Then i plot ACF and PACF. I used ADF test and KPSS test and they said that data are stationary and doesnt need differencing but ...
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Structural Break - Stata

I have used Stata to run a time series multiple regression. I know that there is in fact a structural break in the data and the point at which it occurs; therefore, I have estimated the regression ...
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21 views

moving average: applied to time series equation

If I have an equation representing a time series, such as the following $$y(t) = y(t-1) + y(t-2)$$ But I am not given $y(t-1)$ or $y(t-2)$, so hence this recursive function is not given any initial ...
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Extensions of bsts and CausalImpact to non-Gaussian exponential family distributions

The bsts and CausalImpact packages implement a state space time series model with an optional regularized regression component. ...
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5 views

Estimate of local slope (or tendency to “correction”) in time series

I have multiple time series of values aggregated at the weekly level. In short, I'm interested in finding local estimates of slopes for each week for each time series. An example of one of my time ...
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13 views

Where can I find good references regarding to noise filtering and prediction in time series?

I want to model the error structure of every certain time period obtained from the past errors produced by the predictions of nonlinear time series. I would like to know if someone knows specialized ...
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Advice on imputation of multiple time series

Background In the first year of the study 60 streams had temperature data loggers installed (temperature measured every 30 seconds). The second year only 30 of these same streams had data loggers. ...
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Moving sum window based on the time [on hold]

I have a data frame and there are two variables where one is a numeric(x) and the other is a date(t). I would like to create a new variable which will calculate the sum of X in the last time window. ...
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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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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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1answer
21 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
34 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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36 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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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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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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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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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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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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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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35 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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34 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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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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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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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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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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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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How to analyse transfer function using R? [closed]

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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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
25 views

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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25 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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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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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?