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

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Time Series Analysis

I have attended a lecture about introduction to machine learning at my university (SVM, regression, kernels etc.). Now I'm planning to do a project where machine learning knowledge is needed. In fact ...
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whats the minimum number of time periods needed to get a rewasonable statistical power

I'm running multiple regression analysis with 3-7 indep. variables using macroeconomic indicator data from the World Bank. MOST of the World Bank data sets begin no earlier than 1990, which means my ...
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Augmented Dickey-Fuller Unit Root Test & Cointegration

Using Stata 13. I have a pair of variables (x, y) over time. I want to regress y on x. Do I have to perform a ADF test 1st on x and y to find if both are stationary in their 1st difference (i.e. ...
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Is there a rising trend in Number Series

I am trying to build a stock screening utility. What I am trying is to find if there is a rising trend in a time series of profit margins of a company. I know there can be dips in some years but I ...
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Isolating influence of sampling from actual change

Say I want to evaluate teams' batting coaches in a hypothetical baseball league. It's an unusual league in that there is no control over (and large fluctuation within) the number of at-bats each ...
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How to test predictive power of ARIMA model

Once I've fitted an ARIMA model (by choosing, say, the one with the lowest AIC), how can I go about gauging how effective it is at forecasting a given financial time series? Should I somehow ...
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Census X-11 ARIMA seasonal component identification using R [closed]

I have been trying to wrap my head around conducting seasonal component identification analysis using Census X-11 ARIMA process using R. The procedure is fairly straight forward in SAS(proc x11) but ...
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ARMA Model fitting using statsmodels.tsa.ARMA()

Two questions. 1.) When I use the statsmodels.tsa.ARMA() module, I enter my parameters and fit a model as follows: ...
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Classification on variable-length time series

I have a series of transactions like the following: [0, 2, 2, 3, 1, 0, 0, 0, 1] [1, 0, 0] [3, 3, 1, 1] I would like to classify each transaction as being part of one of two categories: class A or ...
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Unit roots and order of differencing

I'm studying the stationarity with unit root tests and the order of integration in time series $\ln(x)$ and $\ln(y)$ found here. I'm using Dickey-Fuller test with constant but no trend. From what I ...
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advice on a solution attempt: interrater reliability of time-point data

In my data, two coders annotated subjectively (but independently), when certain time-point phenomena (a specific turn in a movement pattern) occurred. The data for the first 14 seconds looks ...
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How is $P[X_t\le x_t | X_1,\ldots, X_{t-1}]=P[X_t\le x_t]$ when $X_t\sim WN(0,\sigma^2)$?

In this slide , p.30 , p.31 , it is written that : White noise : $X_t\sim WN(0,\sigma^2)$ i.e., ${\{X_t}\}$ uncorrelated, $\mathbb E[X_t]=0, \mathbb V[X_t] =\sigma^2$ Example : i.i.d noise : ...
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Estimation of Integer Valued Autoregressive INAR(1) by MLE

Please guide me how to estimate Count data time series Model Integer Valued Autoregressive i.e INAR(1) by Maximum Likelihood Method.
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58 views

Data Visualization: how to plot irregularly spaced time series?

I have a collection of highly irregular sampled data. The gap between measurements can be few seconds, or few weeks or few decades... What are the techniques to plot irregularly spaced time series ...
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Need some help on discrete valued time series forecasting?

I have data on reservation requests for hotels (your booking information:searching date, check-in, check-out, # of rooms and etc. on hotel booking websites) and am trying to do some analysis on one ...
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Condensing spatial time series data and spatial interpolation

I have spatio-temporal albedo (roughly, the 'reflectivity' of earth's surface) dataset, from NASA's MODIS satellite, for a 130 square kilometer area. The dataset contains raster files in the NetCDF ...
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Hierarchical Profiling in Time Series

Is there a source to understand what hierarchical profiling is and how it can be implemented in R? I found the reference to this method here in the following papers: ...
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Time Series Comparison - Correlation and Regression Model

I am trying to see if and how the news for affects the financial markets. I have a time-series for both of them. Should I standardise the series? I have a monthly return on prices from the Dow and a ...
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R: fractionally cointegrated vector autoregressive model with error correction

I can find packages for VECM (vars,urca, tsDyn) but I cannot find: fractionally cointegrated vector autoregressive model with error correction (FVECM). There is a package in MATLAB that deals with ...
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Daily Time Series Analysis

I am trying to do time series analysis and am new to this field. I have daily count of an event from 2006-2009 and I want to fit a time series model to it. Here is the progress that I have made: ...
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How to evaluate double seasonal Holt-Winters model dshw?

I wanna use the dshw method from the R forecast package to predict electricity consumption. I tried to use it on the ...
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1answer
48 views

Feature selection for time series data

I am looking for methods for feature selection (or feature extraction) for time series data. Of course I did some research before, but it was not satisfying. I am aware of methods like PCA, ...
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Smoothing - when to use it and when not to?

There is quite an old post on William Briggs' blog which looks at the pitfalls of smoothing data and carrying that smoothed data through to analysis. The key argument is namely: If, in a moment of ...
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On forecasting, the mean squared error and realized volatility

Say one has finished estimating a correctly specified GARCH(1,1) on a daily time series and now wants to evaluate the accuracy of the one step ahead forecasts what steps or tests could one do? I ...
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How to perform a simple smoothing forecast for next 12 months (using forecast package in R) [closed]

I currently have timeseries data (of gold prices) and I am trying to use a simple smoothing forecast to estimate gold prices for the next 12 months. I am not sure what function to use to accomplish ...
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Stationarity iff causal and invertible

Suppose $X_t$ is an ARMA(p,q) process. Is it true to say: "$X_t$ is weakly stationary iff $X_t$ is causal and invertible"? If so, why? If not, is there something similar?
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Transforming TS for better fit

I'm trying to find transformation for my explanatory variable (outside temperature) to better explain heating power usage. I have data from one year here. ...
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Multiple Correlation Coefficient for Time Series?

I am using Pearsons correlation coefficient to calculate the correlation between two time series. Now I would like to calculate the correlation between a set of time series A, B, C and the time series ...
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What is the use of as.numeric() in R? [migrated]

this is Ex.1 on Page 252 in Statistics and Data Analysis for Financial Engineering by Ruppert: This problem and the next use CRSP daily returns. First, get the data and plot the ACF in two ways: ...
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Subset data in R [closed]

I have several time series and want to regress the dependent variable on the explanatory variables. My question is: Because of structural breaks in my series I do not want to include all the ...
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Detrended Fluctuation Analysis: what does a exponent > 1 mean exactly?

Detrended Fluctuation Analysis is commonly used in order to identify long-range temporal dependencies in time series data. While white noise will have a DFA-Exponent of ~0.5, scale-free, long-range ...
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Correlated time series at different aggregation levels

Correlated time series at different aggregation levels Hi, In my time series data I discovered the following behaviour. My time series are available in 500ms intervals and are about sensor data like ...
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How can I test for changes in the distribution of categorical data over time?

Each week at my organization, we receive X number of type A messages, Y number of type B messages, Z number of type C messages...etc. I want to be able to test if the distribution of these message ...
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deseasonalizing multiple series (more than 200 variables)

I'm trying to produce deseasonalization for multiple series using x-12 ARIMA (as an alternative, if you can manage, you also could provide an idea with other methods, such as x-13 ARIMA). The thing is ...
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Definition of $X_t$ in the context of Stochastic process and Time Series

In the book An Introduction to Stochastic Modeling , Stochastic process is defined as : A stochastic process is a family of random variable(s) , $X_t$ , where $t$ is a parameter running over a ...
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Difference between recursive and rolling window estimation

I am trying to check if my Auto Regressive Distributed Lag (ARDL) model provides stable estimates over time. I am not sure if I should be using a recursive or rolling window method. I know that the ...
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Problems with time series prediction

I got a question about modeling time series in R. my data consist of the following matrix: ...
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R Time Series Forecasting: Questions regarding my output

I'm working on a forecast for the following data: ...
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Hidden Markov model question; pseudo time series?

I apologize that the title of this question isn't super specific, but I am having a very difficult time exactly and succinctly describing the problem I am facing in my implementation of a hidden ...
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Estimate statistical significance of a feature in a time series

I have a set of time series with events marked in the middle. Following the event there is a temporary dip in the series values followed by a peak so that the area under the curve is 0. ...
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Regression Model Data - Changing exponential data into linear data

I have some 20 year monthly economic data that for the first couple of years is growing at a linear rate then grows at a slight exponential rate then in the last few years takes on a linear shape ...
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How to calculate the impulse response function of a VAR(1)? (With example)

How to calculate: 1) Simple IRF 2) Orthological IRF (Y2 -> Y1) Of an unrestricted VAR(1) model: $Y_{1, t} = A_{11}Y_{1, t-1} + A_{12} Y_{2, t-1} + e_{1,t}$ , $Y_{2, t} = A_{21}Y_{1, t-1} + A_{22} ...
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Detrending or Differencing in order to make a series stationary?

I got several time series for which I want to find out if they are stationary or not. So I computed for each series the kpss.test(). But before making further ...
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Is a random walk + white noise modal an ARIMA(0,1,1)? [closed]

Let $Y_t=Y_{t-1}+\epsilon_t$ be a random walk and $Y_0=0$ Why is it true that the process $X_t=Y_t+\eta_t$, where $\eta_t$ is a white noise, so that $cov(\epsilon_t,\eta_s)=0$ for all $t,s$?
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Missing values in Time Series

Missing values are very common in large time series data. How should the missing values of a time series be estimated? Is interpolation useful or I need to forecast them from the past values?
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How do I check for seasonality at different time scales with Excel?

I have a table of e-commerce transactions. Sample data follows: ...
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backward shift operator as a sum (heuristic solusion)

I am interested in converting $(1-L)^n$ to a sum, where $L$ is backward shift operator. Let give you an example, \begin{align} \triangle^1 &=X_{i+1}-X_{i}\\ \triangle^2 & ...
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How can I see sample autocorrelation from time series plot?

Lets say I am given time series plot. How can I estimate $r_1$ and $r_2$ which are sample correlation? I don't understand how to see correlation from data plot. I know how to get and use ACF and ...
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Multiple time points, relation between quantified change in environment and sample population

For a current research, I'm trying to view a relation between a country-level variable index and the financial structure of companies in that country over time. I have annual data for four years, ...
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What does a random walk do exactly?

To be honest, I have read many websites and answers regarding to this question, and none explained it in simple words which are understandable. What I want to do is to understand what a random walk ...