All Questions
6,766 questions with no upvoted or accepted answers
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78
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Hierarchy predictive top down approach
I'm having a problem with using a hierarchical top down forecasting approach. According to my understanding, when I split an aggregated value on the levels below it, I have to know the percentages ...
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859
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Marketing Data with many zeros
I am working on a marketing data which is a time series data with marketing spend done through different channels and revenue generated.
The data looks like this :
My data contains too many zeros (...
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244
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Proc UCM Forecast Series
I'm forecasting a data series with one time dependent variable (GDP) and one 0 1 time indicator "Flag" (0 starting at February 2014, 1 before that). When I use ...
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54
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Seasonal adjustment question, why do days matter?
I have a simple ols equation where the independent variable is total production in a month. I seasonally adjust the data with an x12 and run the ols and get my estimates. The strange thing is, if I ...
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57
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Parafac for crossover design studies
.Hi, everybody
I have data that has 28 subjects involved in a crossover design study including 4 different treatments. The amount of variables is over 10000 (measured almost on the same scale)
In ...
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38
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What are some major theories on picking the right number for the sample window size in time series analysis?
for example the number of samples to run the moving average, or the number of samples for sequential hypothesis testing. Or if there is a control scheme going on what is the best time window for an ...
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291
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Estimate UCM Equation from Ucm model?
Given the output of a ucm sas procedure i need to estimate the equations from the given output , but i don't really know how to do it or where to start. Do you have any hints or anything that might ...
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30
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All of the series used in a model must be stationary at the same order of differencing
While practicing VAR analysis, all of the series used in the model must be stationary at the same order of differencing. Is this correct?
For example, let $X$~$I(1)$ and $Y$~$I(2)$. Can I use these ...
- 1,152
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56
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Factoring Fourth degree polynomial for invertible ARMA process
I have to represent a $MA(4)$ process as an $AR(\infty)$.
In this regard, I need to factorize the polynomial $(1-\theta_1L-\theta_2L^2-\theta_3L^3-\theta_4L^4)$ to have a representation $(1-z)^{-1}$.
...
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567
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Curve fitting in R
I had 4 groups of data (in color 1 to 4) and one group is the data for one day, so I had 4 days of data. I was trying to fit a line which describes the pattern of theses lines (oscillating pattern) ...
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969
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Time series forecasting using SVM
I am trying to set up a Python code for forecasting a time series, using SVM libraries of scikit-learn.
My data contains $X$ values at a day interval for the last one years, and I need to predict $y$ ...
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97
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What non random patterns in a series Autocorrelation cannot detect
I know there are complex patterns in a series that cannot be detected by autocorrelation... but I cannot find what types of patterns these are. Can anyone provide an instance where the autocorrelation ...
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54
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Combine several days of time series into one
I have twenty time series from twenty days. Can I concatenate these time series into one, and run a simple linear regression on the resultant series?
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2k
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Can I difference a time series by 3?
I need to difference a time series to make it stationary.
Is it feasible to difference it by 3?
Example R code:
tsrdiff3=diff(tsr1$LOAD.MW.,difference=3)
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33
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variance of autocorrelated series
I have simulated a series with autocorrelation of 50%. When I compute the variance of the series it is 1/2 of the variance of the white noise series. Could somebody show me the math behind this result?...
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275
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can you analytically solve this bayesian hierarchical model - bernoulli trials
Is it possible to analytically solve (i.e., use a conjugate prior) the hierarchical model shown in the image below to obtain the posterior distribution. The data are composed of bernouli trials ...
- 1,532
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257
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Improving forecasting output obtained from Winter, ARIMA and TBATS method
I am trying to forecast commodity price for next year. I have collected and plotted monthly average prices from last 10 years.Plot has been attached.
I used Holt's-Winter method on prices till 2014 ...
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1k
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Suggestions for Time Series Exercises
Do you know of a good set of exercises, preferably with solutions, that would help me learn Time Series by myself?
I was searching for solutions to the exercises of Shumway and Stoffer's «Time Series ...
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1
answer
112
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Best way to fit an ARIMA model when the values of the variables don't change
I have a time series with various features that record sensor data. It can be the case that the values are recorded although they did not change compared to the previous observation. Hence, the series ...
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1
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84
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Modelling turnovers by a random walk. Is it right?
I need to analyse a bunch of weekly time series that reflect the turnovers of various companies.
I already read that return rates or share prices show stochastic patterns that can be modelled by a ...
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36
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Can I take mean of correlation coeficients for equally spaced data sets?
Historic market (Cash) prices and future contract prices are available for last 4 years. I have found correlation between Jan'11 market price with Jan'11, Feb'11 and March'11 future contract prices ...
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401
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Is this the wrong way to do cross-validation?
I am building an ARIMA model and did a grid search to find which values to use for my AR and MA components using the AIC criteria (this was using all of my data). The results are in this graphic:
...
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266
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Modeling Non-Stationary Time Series Data
Data set: response and predictors are all non-stationary, time series variables
After performing Box-Cox transformations and testing a variety of power transformations on each variable, the non-...
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80
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Associating non-linear three-time-point change with a continuous variable
I would be incredibly grateful for help or advice regarding my following project:
I have 3 time points (0, 30, 120 min) and complete data for about $n=500$ subjects for a continuous variable $M$. ...
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2k
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Fitting an ARCH/GARCH Model (Basics)
I have been given a basic task designed to assess my knowledge of ARCH/GARCH modelling, which involves fitting the models on 2 lots of time-series index returns.
What are the brief steps I need to ...
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34
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Good TS fit but no stationarity
I have yearly time-series that I want to predict, and for that I fitted an ARX (auto-regressive with a exogenous input) model to previous years (training set) and test it for the last year. My ...
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2k
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Does stationary data need to be normal?
So I already ran some tests to make my data stationary. Differencing and box-cox transformation in particular. According to the augmented-dickey fuller test, after performing the above mentioned ...
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1
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96
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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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713
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Comparing bins (quintiles, deciles) based of $R^2$s from multivariate regression
I have a multivariate linear model:
$\mathbf{Y} = \mathbf{X}\mathbf{B} + \mathbf{U}$
where the matrix $\mathbf{Y}$ represents stock returns, the design matrix is constituted by some explanatory ...
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1k
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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 ...
- 302
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3k
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determinstic trend in VAR-models
I'm asking myself the following question. I want to build a VAR-Model with 6 time series A, B, C, D, E and F. I analysed every series univariate and I found out that A, D, E and F are stationary and B ...
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96
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Recommendations for fitting a classifier on panel data to predict one step head forecasted class
I am predicting the binary class, i.e. if it's in top10 or not, of a security based upon it's performance using predictors from current time. So it's simply a cross sectional classifier. As of now I'...
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76
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imbalance in sample size at multilevel longitudinal data
I have longitudinal data (BMI level) measured at 3 time points and subjects are students nested to schools. The sample size in school level differs considerably (n=85 % in school 1, n=10 % in school ...
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391
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Can I use correlation metrics also for time series?
I was using the cross correlation function in R (ccf) until now to discover correlations and lags between two time series.
I was wondering if I can use all other ...
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40
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Fitting a model while adjusting for some variable
I plan to figure out the effect of variable X on variable Y. I have time series data for both X and Y and a simple regression model should do the job. Unfortunately the variable Y is also affected by ...
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560
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How to use R to get drift rate and volatility rate of stock prices changes?
I am doing a research on the historical annual stock prices changes, where I have about 30 rows of annual stock prices. How can I use R to get the drift and volatility rate?
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693
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Timeseries forecasting (Cointegration)
I am trying to forecast commodity price fluctuations in a small dataset. The data I am using is here .
Does my data have seasonality and Trend? Can someone explain me how to decide that?
If my data ...
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0
answers
2k
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Questionable Output from Time Series Forecast Using MSTS and TBATS from R forecast package
Using historical daily order totals, I'm wanting to forecast the totals of the next 7 days. It's known in my field that these totals fall subject to weekly and yearly seasonal trends. Called ...
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51
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the decision of being White noise on e-view
And for example, let's take SMA(2) model
in this table does there exist white noise ? Which value I observe to decide the existance of white noise? Please explain it. Thank you
- 1,152
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148
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How to find anti-correlated subsequences in correlated time series?
Say I have two time series $X_t$ and $Y_t$ (with $ 1 \leq t \leq N$), which have a high positive Pearson correlation. Say I also have reason to believe there are subsequences $X_{tj}, Y_{tj}$ (where, ...
user70001
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302
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0
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156
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Decomposing a known time series into a linear combination of known timeseries
I'm have a time series that is dependent on a large number of other timeseries, but these dependent timeseries don't add up to the main one, as I don't have the full population of these dependent ...
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1k
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how i can model VAR-GARCH
i really need your help
how i can run the ling and McAleer(2003) model (VAR-GARCH) and McAleer (2009) model(VAR-AGARCH) with spillover response?
and can you help me how i can run DCC-EGARCH with ...
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117
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ARMA model in R
I am a bit confused using the arma() function in R regarding interpretation. So what exactly is the equation of a for example AR(1,0,2) given the output AR1, MA1, ...
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65
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Transforming Series with 2nd Moment Nonstationarity
I am trying to induce stationarity in this series. I have graphed a range-mean plot to detect 1st and 2nd moment nonstationarity. Can anyone suggest a transformation that will remedy the 2nd moment ...
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246
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Binary outcome prediction based on multivariate time series in R
everyone!
I have some monitoring dataset for 90 patients. It consists of about 10 parameters (continuous variables) that were recorded each 1 minute 3-4 days for every patient. I know the binary ...
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2k
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Interpreting Correlogram
I am wondering if anyone has any ideas as to how to interpret the following correlogram?
Or in general, what is the best way to treat/interpret a correlogram that exhibits a curve
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166
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Discrepancy between hierarchical top level time series and direct sums - using package hts
I have an xls file with sales data from 12 shops, each selling two types of goods.
If I read in the xls file and sum up sales for each month (ignoring the two types of goods and just looking at the ...
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1k
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Any machine Learning models to predict dates?
I have a general question regarding machine learning models. The idea is to predict what DATE the customer is likely to make transactions or purchases. Variables present in the data set are item, ...
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48
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How long does it take two identical hidden Markov models run on same observations to forget their initial distributions (if ever)?
Let $H_1$ and $H_2$ be two instances of a finite Hidden Markov Model (HMM) $H$. That is, $H_1$ and $H_2$ have identical state spaces $Q$ as well as identical transition $A$ and emission probabilities $...
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