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

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Outlier Detection in Time-Series: How to reduce false positives?

I'm trying to automate outlier detection in time-series and I used a modification of the solution proposed by Rob Hyndman here. Say, I measure daily visits to a website from various countries. For ...
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35 views
+50

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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1answer
393 views

ZScore threshold and low values time-series

Example of z-score computation: 1 - E.g. Time-series: [0, 0, 0, 0, 1] Current: 1 Mean: 0.2 Std: 0.44721 Z = (1 - 0.2) / 0.44721 ~= 1.7888 2 - E.g. ...
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6 views

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 ...
2
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3answers
77 views

Distribution of White Noise in Time Series

I'm a math graduate student and I have to use time series in my thesis. I have not so much knowledge in statistics, but I've studied about probability and time series. So my question maybe can be very ...
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0answers
12 views

Estimation on evolving distribution with small updates

I have a set $X$ of $10^6$ elements and a time series of probability distributions $\mu_1,\mu_2,\ldots$ on $X$. I want to estimate the expected value of a function $f$ over each $\mu_t$. It is easy to ...
3
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1answer
99 views

Multiple ARIMA models fit data well. How to determine order? Correct approach?

I've got two time series (parameters of a model for males and females) and aim to identify an appropriate ARIMA model in order to make forecasts. My time series looks like: The plot and the ACF ...
2
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0answers
18 views

Is an auto-correlation plot suitable for determining at what point time series data has become random?

A piece of research I am working on requires us to decide at what point time series data has become random. For what it is worth, the time sequence in question is a collection of in-process timings ...
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25 views

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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0answers
22 views

Consistent estimate vs out-of-sample performance

When there is a cross-correlation structure in linear regression errors, the usual approach is to model the errors as an ARIMA process. It leads to a consistent estimate of the parameters of the ...
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0answers
4 views

Can Hurst Exponent be applied to non-stationary series?

I have a set of non-stationary time-series which I want to model with ARMA models. Can I apply the Hurst Exponent to the time-series or should I apply it to the differenced time-series (assume ...
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1answer
96 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, ...
3
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1answer
183 views

Nearly constant time series

I want to analyse temporal interactions of some time series by means of the Box-Jenkins approach to find out which time series are predictors of another one (with the help of prewhitening and ...
4
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1answer
53 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 ...
3
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1answer
104 views

First remove seasonal trend or long-term trend in time series?

I have a time series (quarterly data) which has both a long-term trend and seasonality. Taking seasonal differences will make the series stationary, according to the Augmented Dickey-Fuller test. On ...
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0answers
10 views

Standardised residual No Arch Effect

I'm working with bond data and I want to get standardised residuals to conduct a copula analysis. The problem is that often the prices, for consecutive days, are the same and this fact makes the log ...
0
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1answer
76 views

Holt-Winters optimal parameters with gradient descent

Can we use gradient descent in order to find optimal alpha, beta and gamma for Holt-Winters model? And more generally, are there any academic works that suggest methods for finding optimal values for ...
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0answers
20 views

High autocorrelation parameters? [on hold]

I plotted the autocorrelation and partial autocorrelation for two of my time series data in R. But it seems that one of the autocorrelation plots of the two has much higher autocorrelation parameters ...
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1answer
31 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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0answers
16 views

Does a high autocorrelation imply high predictability using an AR model?

Assuming that I have a list of time-series which all have significant autocorrelation at lag 1 and no significant autocorrelation at any other lags. So if I want to test for the predictive abilities ...
3
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1answer
147 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}\\ ...
4
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3answers
193 views

find the point at which the curve significantly shoots up

so this is getting a little complex for me and hope someone can help me out. I do not have a mathematical background. I have a time series of daily rainfall for 50 years for a particular location. ...
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11 views

Evaluating a proportion over time

How do I evaluate a proportion within a population over time? For example, a group of patients undergoes an intervention. Assessment of knowledge is obtained pre-intervention and post-intervention, ...
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1answer
16 views

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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0answers
6 views

Restarting Lag based on Change in Name in Different Column [migrated]

I am trying to insert lags of a variable into a separate column in my data frame on R. However, I want the lags to 'restart' every time the name in a different column changes. An example of data is ...
2
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1answer
99 views

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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15 views

Calculating the optimal Holt Winters parameters (not in R)?

The Holt Winters (HW) technique requires the following parameters: Alpha, Beta and Gamma. The accuracy of the forecasts depends on these parameters. Some software packages (like in R) are able to find ...
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0answers
71 views

Regularization for ARIMA models

I am aware of LASSO, ridge and elastic-net type of regularization in linear regression models. Question: Can this (or a similar) kind of regularization be applied to ARIMA modelling (with a ...
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1answer
23 views

Multi-step ahead forecasting with Weighted Moving Average?

The Weighted Moving Average method is usually used for smoothing purposes. However, it can be used to forecast $Y(t+1)$ based on the last n observed data. In real-world problems, forecasting in very ...
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0answers
27 views

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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0answers
19 views

How should I interpret the results of these two models?

I have a panel data set with two time points: t and t+10. I first ran cross-sectional models for data at t and t+10 separately Y = a + bX b is statistically significant in both models, indicating X ...
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0answers
11 views

How should I test for multivariate GARCH effects for residuals of a model?

I would like to test the multivariate GARCH effect of a multivariate time series. The multivariate Ljung-Box test can do this. However I am also looking for a test to show that a DCC or CCC model can ...
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1answer
41 views

Does ARIMA require normally distributed data? [duplicate]

I want forecast inflation using ARIMA model. My questions are: Does ARIMA require normally distributed input data? (Because my data—inflation—is not normal.) If ARIMA require normally ...
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0answers
34 views

What is a test that I can use to determine if a time series is first-order stationary?

I need to test that one of the time series in my analysis has a constant mean over time. Is there a standard test I can use to help me determine this? I know that I can use a nonparametric procedure ...
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1answer
35 views

On estimating ARIMA models on artificially made time series data

For each day, I observe my variable, y(t), for a period of 12 hours. In order to understand the data and make predictions, I want to put together these data and ...
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0answers
11 views

Multiple event for segmented regression?

Is there any statistical methods like segmented regression for many(>3) events? Recently, many policies in my field were introduced in a short period of time. I usually used segmented regression to ...
0
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1answer
361 views

Handling stationarity issues in proc ucm/state space time series models

Hope I'm able to find someone who can answer this question. The previous one didn't get answered! Proc ucm is the SAS implementation (using state space concepts) to isolate the unobserved trend, ...
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1answer
156 views
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13 views

How to Find the Correlation in Time Series of Categorical Variables in R?

I have a data set of categorical variables occurring weekly. A sample dataset can be found in my previous post. I want to check the co-existence of these categorical variables over time. I want to ...
2
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1answer
125 views

markov chain with probability trends

I have clients with debts that can pass from states own 1 bill, own 2 bills, own 3 bills, leave the service, new debtors and owe nothing. So I could calculate the probabilities of being in state ...
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0answers
22 views

What are some tests for the predictability of time-series?

I have 2500 time series which I want to test the predictability and based on that, choose the best one to forecast. Ideally I want to use a simple model like ARMA-GARCH for forecasting. Are there ...
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2answers
190 views

Does it make sense to use dynamic time warping when clustering time series that all have the same length and sampling interval?

Comparing Euclidean distances with dynamic time warping (DTW): Will Euclidean distance perform better than DTW when clustering time series that all have the same length and sampling interval? Are ...
3
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1answer
377 views

Time Series Data Mining Library?

Can anyone recommend a library for time series data mining tasks other than predictive modeling and statistical analysis? There seem to be a number for these purposes (e.g., Gretl), but nothing for ...
0
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1answer
24 views

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 ...
2
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1answer
85 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 ...
3
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2answers
106 views

Confusing Holt-Winters parameters

I have got a model for forecasting using holt-winters. However the parameters confuse me... The parameters show that there is no trend or seasonality even though there is definite trend and ...
0
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0answers
16 views

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 ...
2
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1answer
97 views

time series - Poor prediction using ARIMA model

I am trying to fit and forecast log returns of a price data using ARIMA model in R. For reproducibility, data is provided here. Steps Followed, Code and Results obtained Check for outliers ...
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1answer
32 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 ...