**8.406 time-series questions.**

I am examining the relationship between rides on a subway and temperature. How can one calculate a correlation with a delay?
For example, I want to see if rides increase after an increase in ...

There are 3 types of tests for the residual autocorrelations here (I have a relatively small sample(58 obs):
...

What do they look like? Is the definition of linearity in time series the same as the linearity in linear regression?

Assume I have date-related data with some periodicity such as used for weather or sales forecasts. From this data I extract features such as calenderweek and keep those numerical, since week 2 is ...

I have a 3D spatial region of size X, Y & Z where each pixel (or voxel) in location $x$,$y$,$z$ has a time series of size $T\times 1$. Time series are highly (cross-)correlated with one another ...

I have time series data that represent a process. The process begins at time 0 and finishes after 400 sec. I have 30 features that I am sampling during the process.
I repeat this process many times, ...

I am asking this with the hope this question can be helpful for me, and for others in my same situation.
I am working for a Company. We mounted some sensors on an industial machine, in order to get ...

I have observed data and predicated data from different models.I did the reduced chi-square test between the observed and model data using the below equation
reduced chi-square = sum{(obs-model)^2/...

I would like to create a linear distributed lag model in order to do some forecast and also being able to interpret the results.
Unfortunately I'm a bit confused with the process I should follow....

I am performing data exploration on commodity futures volatility. I am using data that supports Samuelson's hypothesis that futures contracts expiring in the nearer term have a higher volatility. ...

I can't seem to make sense of the following results. The time-series looks more non-stationary than stationary, and when I fit an ARMA(1, 0, 0) it estimates the AR(1) term to be very close to unity (0....

I'm having a look at the implementation of Autoregression model in Scala https://github.com/sryza/spark-timeseries/blob/master/src/main/scala/com/cloudera/sparkts/models/Autoregression.scala
Now if I ...

I am trying to get a grasp on how to use machine learning to predict financial timeseries 1 or more steps into the future.
I have a financial timeseries with some descriptive data and I would like to ...

So, I'm trying to perform time series forcasting using Keras. So far I get an accuracy of about 45%, and I'd like to know what I could try to improve that.
I've read through quite some LSTM examples ...

Sometimes a time serie may need to be differenced to be made stationary. However I don't understand how second order differencing can help to make it stationary when first-order differencing is not ...

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 ...

I have 3D printer that working exactly 400 second for printing element X [0-400].
The printer produce 30 signals (features like VOLT,X,Y,Z,TEMP etc') in frequency of 50HZ (every sample 0.02 ms) ,for ...

While analysing dummy data for TV shows viewed by user I came up with this graph where I can clearly spot peak hours. As a next step I would like to know "set of ...

Why is it giving me a straight line whereas we can see that there is a pattern? Please tell me what I am doing wrong. I have build this model in python using statsmodel library.
I want the forecast ...

How do I modify BFAST technique in R package?

I want to forecast future(next 20 days) sales with sample dataset.
This is just a sample data and the actual data is from Jan 2014 to Dec 2016. As you can see, sales tend to increase as time goes by, ...

In my studies I've been working recently on dependency between debt and GDP growth in USA from 1966 to 2015. I used logged and differenciated GDP time series data and combined it with 0/1 debt-to-GDP ...

Are there any suggested approaches for using non-stationary series in a VAR model? As per otexts.org:
If the series are non-stationary we take differences to make them stationary and then we fit a ...

I am trying to perform real-time decision making on data from a radar sensor and trying to detect occupancy. I generated data using the same sensor annotated it manually as vacant or occupied. I ...

I am planning to use neural networks for time series analysis and forecasting and i was looking for a reference or any textbook i can use for that purpose. It would be great if it has practical R or ...

I would like to know if there exists a code to train a convolutional neural net to do time-series classification.
I have seen some recent papers (http://www.fer.unizg.hr/_download/repository/KDI-...

I am using a RandomForest to forecast the power in a wind turbine. The results are improving, but i'm getting a slight "lag" between the forecast and the value itself. Is there any way to correct this?...

I have very little knowledge of time-series analysis (despite my stat master - didn't do anything else than an introductory course) but now I'm facing a statistical problem whose answer is this very ...

This is basically a copy of the stackoverflow question here, which never received an adequate answer. The problem is that the Python's statsmodels package insists ...

I am new to time series analysis and related statistical tools. I am struggling with a time series for more than a week. I tried to check the Granger causality for the below data.
...

I am trying to choose the correct ARIMA model.
To get a stationary series on which to plot the ACF and PACF on I've done the following transformations on my original series:
natural log
1st non-...

I am interested in model selection in a time series setting. For concreteness, suppose I want to select an ARMA model from a pool of ARMA models with different lag orders. The ultimate intent is ...

I searched around, but I haven't found a good answer yet.
I work a lot with vibration data, which I analyse and (after the analysis) extract features to model etc.
I have data where a single ...

Question : Analyze and correlate timeseries data which are already binned for every minute (Consider data sources that are stored or used to visualize in tools like Grafana).
Background : Systems log ...

I am researching the best method to use with time series. FBprophet (Python) seems like a strong option.
To prepare time series for Prophet I am thinking about using boxcox and inv_boxcox at the end
...

For the testing purpose, I have created two time series object with same frequency (frequency=12) using xts() and ts() from the ...

I'm working on a project of forecasting turnover on retail industrie.
I have the turnover of different products for a 2 years period of time
My final goal is to forecast a global turnover with and ...

I have consecutive measurements of two different phenomena (x and y) on the same subject, giving me a collection of time-series x_ik(t) and y_ik(t), where i is the i-th measurement and k is the k-th ...

I am looking at fitting a Generalized Pareto Distribution (GPD) to extreme events which exceed a certain value threshold for Bilbao waves data.
Selecting threshold at c=7.5, resulting in 154 ...

I have a zero-mean stationary (weak) process ${X_t}$ (meaning $\operatorname{cov}(X_t,X_{t+\tau})$ is a function of $|\tau|$ only for all $t$) and from it we get $Y_t$ such that $Y_t = X^2_t$ . In ...

I have a data set including daily prices and demand of a commodity. I am sure that, price and demand weekly and monthly changing. So it has a seasonality effect. How can I decompose it by using daily ...

I want to use CNN architectures for classification of multivariate time-series, where we apply one label to each sequence.
I searched the net for the available designs in the literature and i found ...

Suppose that $\{X_t\}$ is a weakly stationary time series with mean
$\mu = 0$ and a covariance function $\gamma(h)$, $h \geq 0$, $\mathrm{E}[X_t] = \mu = 0$ and $\gamma(h)= \operatorname{Cov}\left(...

I've got this prediction problem for daily data across several years. My data has both yearly and weekly seasonality. It's also stationary.
I tried using the following recurrence:(which I just came ...

Data set: multiple product sale volumes by month (product A, B, C, D, E)
I want to analyze the data and find out the seasonality component for each product relative to each other (for example ...

I have lots of pairs of timeseries e.g.:
I am trying to get an idea of how correlated they are (the black fit lines rather than the data themselves). I thought that one way to do this would be to ...

I have a time-series dataset and I am required to find similar clusters in the data.
Based on my current knowledge and the requirements of my application, I used ...

I am performing linear regression in order to see the relationship between Air traffic demand, GDP per capita and fares of ticket.
I am interesting in getting elasticities therefore, I used the ...

I have a daily time series that I am having issues forecasting accurately.The time series is stationary and it looks I have tried ARIMA(3,1,1),(0,1,1)- 7 Period, auto.arima(D=1), Holt-Winters, nnetar,...

When a parameterized data model and corresponding pdf are known, the CramÃ©r-Rao lower bound provides an lower bound for the variance of an estimator of one of the parameters. That is, given the data ...

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