time-series's questions - English 1answer

8.827 time-series questions.

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I am trying to model gas consumption in France. The industry publishes a formula to use for this. A simplified version looks like this consumption = K * f(x), where ...

I want to test a model I have on a time series. The model is that the time series adapts to a trend $f(t)$ with a speed $\alpha$. There is also noise in the model. So, the time series is a function ...

I am comparing different exogenous variables in how good they support the forecast of the monthly seasonal adjusted unemployment rate. All my data is monthly (2006-01-01 until 2018-09-01) and ...

I have the following system of equations: $$ \begin{align} y_t^{(1)}&=y_t^{(2)}-x_t+\epsilon_t\\ y_t^{(2)}&=x_t+\nu_t\\ x_t&=\alpha x_{t-1}+u_t \end{align} $$ where $y_t^{(1)}, y_t^{(2)}$ ...

Consider a case where I have a time series data but no information about what the data is about and its frequency. Now I use findfrequency() function of forecast ...

I have done a training set to fit different ARIMA models and then a test set to assess their performance (with R). From what I understood, I can use the AICc to determine the best model by choosing ...

Sorry in advance if this is too basic of a question - I've been struggling with this data set for almost a month and feel like I'm going in circles, and the more I Google the more confused I get. I ...

I have two series of daily close prices for UN and UL from 01/02/2002 to 12/31/2002. Both are for Unilever Co. When I conduct the Engle-Granger cointegration test, the MacKinnon $p$-value is high, ...

I have developed a time series model using BSTS package in R and set the seed to some constant value. When I rerun the model after few days using the same training data, the model coefficients are ...

Data: I have a time series data of 2528 daily observations for OMXS.30 (Stokholm) closing price. The aim is to fit proper ARCH/GARCH models and use for forecast daily Value at Risk. Here is a plot of ...

Stambaugh bias definition

2 answers, 2.261 views time-series
How would you go about explaining "Stambaugh Bias" in simple relatively non-technical language?

Recently I saw a person's resume online and it is said he used ARMA and Gamma model to analyze time-series pattern of bond market volatility.I know what ARMA model and Gamma distribution is but not ...

Is the result obtained in findfrequency() function of forecast package and the frequency parameter of ts() the same?

I'm working through the book An introduction to state space time series analysis by Commandeur and Koopman, and I want to replicate a few of the simple models in Stata 13.1. The two related models I'm ...

I'm in the beginning of learning about time series and what i just cant grasp right now is what to do if my data is not stationary. Any ideas?

What is the stationarity/convergence restriction for a threshold heteroskedastic model, TGARCH, process? I know that for a GARCH model: $\alpha+\beta<1$, but I'm guessing it's not that simple for ...

I have a data set of weekly sales for a range of stores (all belonging to one company). I am trying to predict weekly/monthly use of several ingredients in the individual stores. The choice for what ...

In the paper "Clustering of Time Series Subsequences Is Meaningless" Keoh et al. claim that breaking a time-series into chunks (sometimes called lags) of fixed-size using the rolling window method ...

Above is output from SAS. What would be the corresponding ARIMAX equation? I would appreciate if someone could help me write the mathematical equation, preferably in the following form: $$ Y(t)= ay(...

Does standardizing of a dependent variable within the identifying group make sense? The following working paper (Deforestation slowdown in the Legal Amazon; Prices or Policies?, pdf) uses a ...

My question relates to excursion areas (and excursion bridges, in particular) for $\chi$-, $\chi^2$-, gamma- and/or F-distributed stochastic processes (or more general random fields with positive ...

I tried to use Holt-Winters for forecasting, but it gives me negative values, but since these are demand quantities they cannot be negative. ...

I am working on a forecasting model for natural gas consumption. I have many exogenous variables and when I train the data with the nnetar model(using R and the forecast packagae), one can specify the ...

Let's say I have a time series data set consisting of features that may correlate to whether or not the price of a stock will go up or down. Say these data points are at 5 minute intervals. I build an ...

I have a neural network I am training on some time series data. Naturally I want to sequentially mini-batch this data if at all possible. However, it seems that if the data size isn't a multiple of ...

We got a set of data showing the gender distribution of a course each year. (X, Y): X is the number of male while Y is the number of female. (20, 35) (24, 43) (10, 50) (25, 67) (19, 65) (30, 53) (33,...

I'm working on anomaly detection methods for multivariate time series $[\mathbf{x}^{new}_1,\dots,\mathbf{x}^{new}_T]$ where $\mathbf{x}^{new}_{i}$ is $p-$dimensional. I won't go into the details of ...

I know this question is kind of complicated to understand at first. Here's the deal: I've got organized, monthly sales data from various years, and when I graphed it I saw there's (obviously) a ...

Like other time series problem, it's a location equal spaces time series P(x,y,z). Obviously it has three coupled parameters (x,y,z) at each time point. It can't be resolved only by three separated x,...

A random walk without drift is not stationary. Because its autocovariance function depends on time. A random walk with drift is not stationary as its mean is not constant. But what is the ...

I have multiple time series matrices - belonging to different subjects - and would like find a function that finds clusters of similar values. It is my understanding that one can perform a Cluster ...

Basic problem: I need to predict the temperature for the next 120 hours. I have historical hourly temperature data. It's easy to predict 120 hours using ARIMA, but I want to incorporate a local 5-...

I've assayed two treatment groups at three time points. The time points are "days post inoculation" (specifically, 0, 20 and 40 days), so the interval between the measure is meaningful. I've ...

I have done my best to understand how to do it in a proper way but I have still a lot of doubts. I have two time series of counts. My a priori hypothesis is that the second time series depends on the ...

i was working on some financial data on matlab and using it for time series forecasting. there is a function in it periodicreturns(TotalReturnPrices,Period). if i choose a period of say 5 days it ...

I want to compare two time series and want to find out if there is a significant correlation. Specifically I want to find out if in certain situations there is a specific lag between the series. I ...

I have the following graphs and I need the X_{t} and Y_{t} graphs and ARIMA(p,q,d) models to which the graphs correspond? Does anybody know the graphs or does anybody know how to do this?

I am conducting a study on the value of Bitcoin in the period between July 7th 2018 and October 8th 2018. My independent variables are number of discussion and sentiment of discussion about Bitcoin on ...

I'm analyzing a time series that has this format: ...

The documentation for BSTS says the following about coefficients If object contains a regression component then the output contains matrix with rows corresponding to coefficients, and columns ...

I am running an ADF test in R on the following series: This to me is clearly non-stationary, but when I run the ADF test: ...

Please, help me guys! I do not know how to answer the following questions. Let $y_t$ be a stationary variable. Consider the GARCH in mean model and answer the questions: $y_t=c+\phi_1 y_{t-1} +\...

I would like to simulate two AR(1) process with a specified correlation and with a condition such that one process is greater than the other. Specifically, I would like to Simulate two processes $X_t=\...

I was working through my textbook and found this problem that I got stuck at: Consider the AR(2) Model $$X_t = \phi_1X_{t-1}+\phi_2X_{t-2}+\epsilon_t$$ We can assume $\phi_2 > 0$, so the roots of ...

I have a small number I(1) time series (under 10) that are cointegrated. I would like to create a forecasting model and my choices are either cointegration or regression of differences. I understand ...

I would like to decompose a time series $S_t$ into two components: $$S_t = X_t + M_t$$ with: Mean-reversion, modelled by an Ornstein-Uhlenbeck process $dX_t=-\alpha X_t dt + \eta dW_t$ Stochastic ...

Hello, all. I am asking this question in not necessarily a "subjectively recommend something for me" approach, but with a clear focus on just an accessible beginner's reference. My situation is I have ...

I was going through these problems and think I figured out most of them both, but am having some troubles at one of the last steps. The question is for each of the following models: Express them ...

Lets say I have two annual time series - one is in-situ measured Sulphur input to given watershed and the second one is some index (number) acquired via remote sensing (satellite images) over the same ...

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