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

learn more… | top users | synonyms

0
votes
0answers
4 views

Can I just use Adaptive MCMC for Everything?

In time series econometricts and finance, most Bayesian authors estimate their models with a Gibbs Sampler, this is especial true for state space models, SV and so forth. The dimensionality of these ...
1
vote
0answers
19 views

Methods to find the correlation between one variable and a set of variables

I have discrete measures (let's say $I(x,y,t)$, i.e., coordinates in space and time) on a geographical map that are sampled randomly. I also have a constant flow of optical images in several ...
1
vote
0answers
11 views

comparing multiple proportions over time

I have a dataset of 22 fish species in a fish market, sampled monthly, from 2007 to 2011. I want to see if there is a statistically significant change in their relative proportions over time. I first ...
1
vote
0answers
24 views

Can we estimate VAR model using monthly data for 7 years?

I am interested in estimating the long run relation between electricity shortfall and industrial output with other variables. It forms a 6*6 matrix. Can I get reliable estimates? I am very much ...
2
votes
0answers
29 views

GARCH vs SV for Forecasting

I believe I am aware of how GARCH family and stochastic volatility models differ in their construction and assumptions on the volatility states, (i.e. GARCH family assumes deterministic volatility ...
1
vote
1answer
15 views

A smoothed series still exibits strong seasonality

I have a monthly time series. It is basically a price level series (inflation data), and I converted it into monthly percentage changes (i.e. like the CPI measure). This time series exhibits extremely ...
1
vote
1answer
188 views

How can I recognize when I must apply “log transformation”?

I have some time series - http://ww2.coastal.edu/kingw/statistics/R-tutorials/simplenonlinear.html In this article author try to use log transformation for pressure data. How can I recognize that ...
1
vote
0answers
28 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
votes
3answers
98 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 ...
2
votes
0answers
31 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 ...
1
vote
0answers
23 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 ...
0
votes
0answers
6 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 ...
1
vote
0answers
12 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
votes
0answers
41 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 ...
1
vote
0answers
17 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 ...
0
votes
0answers
12 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, ...
0
votes
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 ...
0
votes
0answers
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 ...
0
votes
0answers
20 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 ...
1
vote
0answers
12 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 ...
1
vote
1answer
43 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 ...
2
votes
0answers
37 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 ...
0
votes
0answers
12 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
votes
0answers
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 ...
1
vote
0answers
23 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 ...
1
vote
1answer
24 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 ...
1
vote
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 ...
0
votes
0answers
16 views

Creating a Volatility Index [closed]

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 ...
1
vote
2answers
55 views
1
vote
0answers
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, ...
0
votes
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 ...
0
votes
0answers
25 views

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 ...
0
votes
0answers
15 views

Nonlinear forecasting methods [closed]

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 ...
0
votes
1answer
25 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 ...
0
votes
0answers
25 views

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 ...
0
votes
0answers
16 views

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 ...
0
votes
1answer
26 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 ...
0
votes
0answers
10 views

Shock event values in Linear Aggregate Definition of AutoRegressive Process

I am a 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 ...
0
votes
1answer
18 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 ...
1
vote
0answers
18 views

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 ...
0
votes
0answers
23 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 ...
0
votes
0answers
9 views

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. ...
0
votes
0answers
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 ...
1
vote
0answers
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 ...
1
vote
0answers
15 views

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. ...
0
votes
0answers
8 views

Moving sum window based on the time [closed]

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. ...
1
vote
0answers
15 views

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 ...
2
votes
1answer
88 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 ...
0
votes
1answer
23 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 ...
1
vote
1answer
34 views

Am I causing statistical violations? [closed]

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 ...