**8.547 time-series questions.**

I have time-series data generated via Metropolis algorithm - Monte Carlo simulations. Since these data must have some correlation between them, the formula of the standard error for IIDs variable must ...

I have Data on Prices of house. Along with these variables.
1) Location i)Latitude-Longitude
ii)City and State 2) Attribute of house.
...

I have learned about the Generative Adverserial Networks and the way they are used for learning the underlying (complex) distributions of high dimensional data.
Now, my question is: Are there ...

I have two time series, spanning about 2.5k observations each. One series is observed values, the other one is predicted values based on a simple linear regression model. Both time series share the ...

I am studying how to use the pettitt.test function from the trend package in R to detect change-point in a time-series. However, ...

In my experiment, individuals assign probabilities to the likelihood of future events, and update their forecasts as frequently as they like. Most questions stay open (receiving new forecasts) for ...

Dr. Ole Peters presents the concept of (non-)ergodicity with the following gambling example:
You're given $\$100$ to play a game where you toss a coin once a minute. If it comes up heads, you win $50\...

I am interested in determining if the decisionmaking of a particular government body was responsive to policy and statutory changes that occurred at known points in time.
I can classify the decisions ...

I am looking through Time Series Analysis: With Applications in R (my first exposure to time series) and refreshing summations.
I.
When given the following rule:
COV[$\sum_{i=1}^{m} c_{i}Y_{t_{i}},...

I noticed that in many tutorials with neural networks people difference their time series prior to training/forecasting.
Suppose that we have a window model with many autoregressive terms (say 365 ...

I have the below time series data:
I want to explore the relationship between dependent variable y and the independent variables x1 and x2. My aim is not forecasting. Just finding the relationship ...

According to Caret's documentation, the train() function uses all training data to fit the final model when best hyper-parameters have been chosen.
However, when a ...

Long story short, I'm trying to predict how likely it is for a content creator to release new content or when they are most likely to do so (and possibly how this changes over time). My problem is ...

Suppose a time-serie like this on the left-top corner with weekend and daily fluctuations. This time-series need differencing due to the rising ACF (bottom-left) and portmanteau tests' p -values too ...

I am after some suggestions on what statistical analysis I can perform to show a before-and-after effect in a longitudinal electronic healthcare record (EHR). I have N number of EHRs, of varying sizes/...

I'm asking this within the context of time series, but the question would apply to any regression type problem.
It usually specified that using information criteria like the AIC or the BIC to ...

I am looking for longitudinal/panel data following the voting history of individuals in the UK electorate. Through the British Election Study and the UK Data Service I only seem to be able to find ...

I tried to compute parameters of ARIMA/GARCH in two step. The first one is to build ARIMA and then fit GARCH using iid Gaussian MLE estimation. The second one is to construct ARIMA/GARCH ...

When training an RNN for time series prediction, what can one expect to see visually as the model learns? In particular, are plateaus a normal indication that the model is underfitting or do they ...

I have a time series of sensor data from a machine. This machine is sometimes moved and thus there are big chunks of missing data, here is a plot of the data points:
My goal is to try to start ...

Suppose I have daily time series data and I want to predict a month in advance using a set of features. I have lots of them so I'll be using regularized linear regression. To create the response I can ...

I'm trying to model the responses from a direct mail marketing campaign so that I can use it to forecast for future campaigns.
I started, in the code below, with the average number of responses by ...

How could I prove that AR(1) model with time trend: $y_t = a_0 + a_1t + a_2y_{t-1} + e_t$ to be in the form of a MA model?

As a beginner to time series analysis, I'm trying to understand the best way of detecting the points at which my univariate time series shows a change in trend direction (see highlighted example).
I ...

I have daily data on how many people entered a certain shopping center, and the weather on that day. I wish to find out if there is a relation between the weather and the number of people who entered ...

As the title, really. In my time series class, we only ever covered using using the option constant, and never trend. But now when playing around, I notice markedly different results. Ie, if I use ...

I'm a beginner at Econometrics, and I'm trying to learn the main econometric techniques in R. My doubt is on how to normalize the cointegration matrix to ensure ...

I have a revenue dataset for various businesses. For about half of those businesses, monthly data is available. For the other half, only annual revenue data is present. I know the seasonality of the ...

I have a very noisy time series like this and I forcast future values with auto.arima from the forecast package in R:
...

Playing around with auto.arima to see how effective it is at model selection. I first simulated an $AR(1)$ process with $X_{t+1} = 0.9 X_t + \epsilon_t$
...

Comparing time-series data for single day of week
I've been provided time-series data for customer wait time (seconds) taken at 5 minute intervals for 15 separate Tuesdays from this year (midnight to ...

The question is:
"Suposse that: y$_t$=$\beta$y$_t$$_-$$_1$+s$_t$e$_t$; e$_t$~N(0, $\sigma$$^2$)
s$_t$=exp{$\beta$y$_t$$_-$$_1$}
Derivate the log-likelihood function for y$_0$=0
Assume that $\sigma^2$...

I'm following the procedure in this post (Adjusting daily time series data for the seasonal component) on R 3.2.3 (linux).
The de-seasoning process in the above post works fine. But with my data, a ...

I am trying to refine the way my company validates tests in retail stores for products that we sell. The prior way was only to look at immediate change in dollar and unit sales without taking into ...

I have two variables. Both are I(1), so non-stationary in levels but stationary in first differences.
However, having run some tests, I find that both are co-integrated. Based on my statistics ...

So, I have a time series of historical data on some online ads. These ads are "cost per click" ads - we pay only when a user clicks on them. For the last 3 years, I know how much we spent on each ad, ...

I am trying to understand how to identify lag length to use for a Granger Causality test. The process as I understand it is:
Use an information criterion such as AIC or BIC to calculate the number ...

I am working on a problem of predicting event counts based on user history.
This is a classical time series analysis problem, and I used the ARIMA model: (wiki).
I also applied a Hawkes point process ...

I have time series data, of some numerical measure. The time interval is in seconds (although I have gaps in the data - missing values). I have data from 2 consecutive months, while for each month I ...

I am trying to characterize the drivers of change in tree mortality through time (2011, 2013, and 2015) using a suite of explanatory variables. My datasets looks like this, as an example (not real ...

I have a timeseries which is clearly seasonal and has trends. I would like to treat the data (e.g. differencing), to get a white noise autocorrelation plot.
Here is the autocorrelation plot for the ...

I wonder if anyone can help.
I have a set of data on event ticket sales. I have information on eventdate, location, capacity, cumulative sales, sales date, total sales.
I want to be able to build a ...

Consider a simple regression context in which there is a small set of response values, $Y$, and corresponding dates, $X$. (For simplicity, we can assume the dates are equally spaced.) We would like ...

I am new to time series Analysis, and I have noted that there's only two kind of models: Additive or multiplicative.
I want to know if there's other cases where we can find a combination of both.
For ...

I am looking at finding correlations between house price time series and the time series of multiple indicators in an area. For example
These two trends clearly show a sort of strong negative ...

I want to estimate the current maximum capacity (in kWh) having the current power consumption (in kWh) and the state of charge of the battery (in %) available in a time series.
I do not have a full ...

I have worked on SSM model using KFAS package (https://cran.r-project.org/web/packages/KFAS/KFAS.pdf) in R. Package suggests me to use one of the Box_Jenkins method to implement SSM. So we convert ...

I have conducted an experiment whose objective is to assess the collected data's patterns and to tell if the data is in general increasing or decreasing and, if possible, to compare the "trend" of two ...

I am currently experimenting with neural nets for classification of on-line handwritten data (hence: not pixels, but time series data). To do so, I use several toolkits (internal development of my ...

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