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

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heteroscedastic time series in SAS autoreg - white noise matter?

I normally work with categorical outcomes, so a lot of this is new to me. Attempting to model monthly interrupted time-series in proc autoreg. There were 11 intervention changes of varying potency ...
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88 views

Inferring likely dates based on other related dates in incomplete data set

I'm taking my first steps in data science and machine learning. I'm experimenting with a project where I have no idea even what approaches I might start with, so I'd appreciate any leads: I have a ...
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125 views

Forecasting technique for daily data with monthly and day of week seasonality

I have daily data for 3 years. This sales data is of seasonal nature as business has spikes and downfall by month. Also, sales differ by each day of the week. for example, monday in general in a month ...
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130 views

Convert double differenced forecast into actual value

I have already read Time Series Forecast: Convert differenced forecast back to before difference level and How to "undifference" a time series variable None of these unfortunately gives ...
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18 views

Real time Causality calculation for financial time series

I have 0.2 million financial time series data (1-minute data, each minute 1 sample data point), I want to find the causality in real time like if someone give me a data point how I know that one point ...
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41 views

Causality between multiple time series

I have 1000 financial time series (closing prices), I am using Toda-Yamamoto test. It is impossible to calculate the causality manually as there are $C_{1000}^{2-1000}$ cases. Is there any way in R ...
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37 views

Machine Learning Regression models give same conclusions with very different premises

I have developed a model (TSM) which is very good at forecasting daily revenue, however it is very black box. The TSM is univariate, whereas the regression models are multivariate. My goal here is to ...
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63 views

Why can correlograms indicate non-stationarity?

I'm reading about correlograms, and how they can be used to detect non-stationarity. Supposedly, if the autocorrelation constant is significant, and/or declines slowly, we would deem the time series ...
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36 views

Interpretation of ADF(Augmented Dickey-Fuller) and KPSS (Kwiatkowski–Phillips–Schmidt–Shin) tests for time series

Can anyone please clarify for me the differences between ADF (Augmented Dickey-Fuller) and KPSS (Kwiatkowski–Phillips–Schmidt–Shin) tests in testing the stationarity of a time series? I tested my ...
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56 views

Interpretation of ADF(Augmented Dickey-Fuller) and KPSS(Kwiatkowski–Phillips–Schmidt–Shin) tests for time series [duplicate]

Can anyone please clarify for me the differences between ADF(Augmented Dickey-Fuller) and KPSS (Kwiatkowski–Phillips–Schmidt–Shin) tests in testing the stationarity of a time series? I tested my time ...
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40 views

Can my data be described as a random walk or not?

I'm trying to figure out whether the observed "time series" can be described as a random walk or not. Unfortunately a major problem regarding my data is in time intervals: Problem: Time ...
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59 views

Forecast and 95% confidence interval

I have this homework about time series econometrics and I am having some difficulty trying to solve it. Can somebody can help me understand how to start and solve it? This is the text of the ...
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32 views

Ljung Box test chi square distribution

I want to prove the following statement: Under $H_{0}$ the test statistic $Q=n(n+2)$ $\sum \limits_{k=1}^h \frac{\hat{p}_{k}^2}{n-k}$ follows a $\chi ^2(h)$ chi-squared distribution with $h$ degrees ...
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37 views

Times series homework: VAR model

I am looking for help in understanding my homework. I do not understand how to solve this problem. I hope some of you can help me on how to approach it or suggest some references.
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1answer
65 views

Detect outliers (anomalies) in salary data

I have a dataset with over 10,000 employees and I'm interested in performing some tests to identify salary anomalies. For example, paychecks for some employees might look like this: ...
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1answer
39 views

Forecast a song ranking for tomorrow

I need to forecast the position of a song on a music chart. EXAMPLE: chart from #100 to #1 my song started from #100 and raised during the days my song today is #10 and i know the movements and the ...
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44 views

Use of z-score to normalize two different datasets (price and volume) for comparison

Background: I'm trying to look at the correlation between two datasets, one consisting of price and the other of volume, for a commodity over a period of time. The data are not normally distributed. ...
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65 views

cointegration and correlation

In the following post it was shown by mpiktas that the sample correlation of two I(1) series converge to a random variable. On the other, given two cointegrated I(1) variables the OLS estimator is ...
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45 views

Estimating Cointegration vector

I am learning about the concept of cointegration and I found in various places the following claim about estimating the cointegration vector using OLS which is: Despite the fact that the OLS estimator ...
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52 views

Multicollinearity problem and differencing time series

I have to estimate the regression equation by OLS and do in-sample forecasting of the time series. It has a trend and seasonal variations. So I try to estimate the model which looks like $$\text{ln} ...
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27 views

Generate multivariate time series

Suppose that I want to generate tri-variate Gaussian time series $\{(X_{1i}, X_{2i}, X_{3i}), i=1,2,...,n\}$ with a correlation structure across the three time series; that is, $(X_{1i}, X_{2i}, ...
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1answer
62 views

Predicting running times for track meet places

Good morning all, I'm a track/running coach in Texas. That's my gentle way of saying that I don't have a statistical background, but I'm trying to learn what I can. Forgive me if I don't use the ...
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40 views

how can I focus the log rank test in a selected period of time of follow up?

I am using R survdiff (survival package). I would like to focus the analysis on the first 2 years of my survival curve (that is actually much longer, but with few cases in the long term and with ...
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1answer
21 views

How do I update a Holt-Winters model in R? [closed]

I am currently using Holt-Winters model to predict time series data. When new data becomes available how do I update my model to account for the change in seasonality and trends?
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35 views

Test of significance between autocorrelation coefficients for two time series

I have a time series measured at monthly intervals, and I want to determine if the first half of the series is less persistent than the second half of the series. My initial thinking was to the ...
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27 views

Prediction or forecast error

I understand the general idea of different time series model fittings, calculations, and model comparison. However, I am a little confused of understanding the forecast of a time series model. For ...
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54 views

Regression with cointegrated regressors

Suppose that we have the regression model $$Y(t)=\alpha +\beta_1X_1(t)+ \cdots +\beta_nX_n(t)+\epsilon(t)$$ One approach to fitting this model is to use OLS. If the predictor variables ...
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78 views

How to choose automatically between Auto.ARIMA, ETS and STL in R

I'm working on a sales forecasting package which should be easy to use for the end user. Given a time series with historical sales data I would like to automatically select one of the three forecasts: ...
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25 views

Time Series Shocks with Exponential Decay

Imagine a piano key played in an auditorium: The amplitude of the sound wave is perhaps highest in the first milliseconds, then slowly decays to zero if no other notes are played. If other notes are ...
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24 views

ARMAX / ARIMA models: Effect Size and R-squared

Is there an easy way in Stata to get the percentage of the variance explained by an ARMAX/ARIMA model (similarly to the adjusted R-squared in multiple linear regression)? Moreover, working with ...
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40 views

Time-dependent Poisson regression

I have a time series that count the number of "type 1" events in a city, for each day. The serie contains a lot of zeros because type 1 events are rare (about 80% of counts are zeros). I'm using a ...
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12 views

Uncertainties with how to run a VECM for testing the J-curve

I am attempting to test for the existence of a "J-curve" effect (deterioration of the trade balance in the short-term after the depreciation of the currency via more expensive imports, followed by an ...
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68 views

Time series object for hourly data in R

I am using forecast package for time series analysis. I have a dataset with hourly data from 1 June 2015 to 30 November 2015. The dataset exhibits weekly and daily seasonality. I used the following ...
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31 views

Time series with ARIMA(0,0,0) with non-zero mean

How to interpret and fit the model for stationary time series of frequency 1? The auto.arima output is "ARIMA(0,0,0) with non-zero mean". Forecast is giving 0 only ...
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23 views

Some, but not all, panels seem to cointegrate

I am trying to estimate the long-run relationship between equity prices and bond default risk measures using a 250*250 panel of firms. There is strong theoretical evidence that there is a relationship ...
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25 views

Predict how many people are going to book for a given trip on a given date

I need to build a model predicting the nr of people that are going to book for a given trip on a given date. The prediction must be made at least 4 months ahead of the departure date. Could you please ...
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Medical data: is measuring and comparing relative increase okay?

Context: 30 patients each use a sensor daily, but the sensors unfortunately are not calibrated on the same scale. Interested in "improvement" over time amoungst groups of patients. Question: To ...
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26 views

Time series or multinomial logistic regression?

I have a doubt about a modeling approach. My dependent variable has multiple phase such as 1st phase, 2nd phase, 3rd phase, etc.. and every stage is dependent on its previous stage/stages. The ...
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23 views

Differences in Gaussian and Student's $t$ GARCH coefficients

I am working on differences between GARCH(1,1) model with Gaussian innovations and Student's $t$ innovations. This is the output for Gaussian GARCH: ...
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24 views

Frequency parameter and its impact on auto.arima results

auto.arima returns two different models weather I define my time series with frequency=1 (default value) or ...
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18 views

deterministic time trend vs stationarity

Sorry for the newbie inquiry but I'm having a little trouble making sense of stationarity and how a the presence of a time trend impacts this. I'm working on a model for operating margins and as a ...
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47 views

Simulating a time series including a shock

I want to simulate a time series in R, following an ARMA(1,0) model in the form $Y_t = Y_{t-1} + \epsilon_t$, shocking it at time 20. In a few words, I therefore have to input $\epsilon_{20} = 30$ ...
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35 views

How would you interpret these results for garchfit?

Im trying to fit a garch model to TESLA time series. Here's the code I used on R: # ...
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20 views

Sequential semi-automatic model selection of time series forecasting

I have a number of univariate time series that I would like to incorporate in a production system. I have daily data from a month and I would like to forecast every day the corresponding values for ...
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14 views

Analysing time series of unequal lenght

Hej all, I am analysing the pattern of snow melt with 5 replicates, at each of which there exist two experimental sites: one control, and one fenced exclosure for herbivores. I would like to ...
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1answer
31 views

Log-transforming time series data before cointegration testing

I am testing the cointegration between these variables: Gold Price (Ringgit), Exchange Rate - MYR to USD (Ringgit), Real Effective FX Rate Based on CPI, T-Bill 10 Years Rate, Consumer Price Index. ...
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54 views

When is an “ARIMA process” stationary?

I'm sorry if this is a duplicate, but I can't seem to find the answer to this. If $Z_t$ is a white noise process and $X_t$ satisfies $$ \phi(B) X_t = \theta(B) Z_t $$ (where $B$ is the lag ...
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Are time series data produced by time-invariant simulations stationary?

I'm looking for insight into a weird problem that's on the interface of kinetic modeling and statistics. Suppose you can simulate the concentration of two molecules $X_1$ and and $X_2$ with a system ...
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21 views

To fit a GARCH on ARIMA residuals or to fit an ARIMA+GARCH

I am working on time series data and have both conditional mean and conditional variance in the process. My strategy has so far been to fit a GARCH on the residuals of a fitted ARMA model. But then ...
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Autocorrelated cyclical component of Hodrick-Prescott filter

I run a linear regression of the cyclical component of the Hodrick-Prescott filter and I obtained the following graph of the autocorrelation and partial autocorrelation of the residuals: My ...