Questions tagged [time-series]

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

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Difference-in-differences analysis with multiple time periods

I am at the effects of a law on listed companies. I have a control group and a treatment group, but instead of having a pre treatment period and a treatment period I have 3 time periods. I have ...
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10 views

Should I check structural breaks when forecasting, given limited time window

I attached the graph of a time series below, which is a series of the probability of defaults. This is a quarterly time series. When I am using a regression model to forecast this time series, should ...
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Do we have to make cycles and trend covariates/predictors stationary in order to use them as valid predictors in a Dynamic Regression Model?

I want to extract patterns from macroeconomic indicators for use in predictor a target variable. In particular, I plan to decompose the macroeconomic variables into trend, cycle, maybe seasonality and ...
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9 views

Interpret interaction terms in Poisson

I'm using GLM to represent a Poisson model with the log link function. The response variable is a count variable representing the number of deaths. Both temperature and season are explanatory ...
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In econometrics VAR model OLS equation what is it mean

enter link description here please briefly describe
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11 views

Resampling methods for curves and time series

In the case of imbalanced datasets, different oversampling/downsampling methods exist such as SMOTE, ADASYN, etc. However, this methods mostly simply interpolate in the feature space, treating the ...
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18 views

What statistics can I use to describe the shape of a time series?

I want to apply a clustering algorithm to some time series datasets. I've tried DTW, but it hasn't quite achieved what I want (which is to cluster similarly behaving series such that I can tune ...
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1answer
35 views

How to analyze the relationship between two variables in a time sequence

I have a question about how to analyze the relationship between two variables in a time sequence. It is an eye-tracking experiment. I recruited two separate groups of Mandarin speakers to describe ...
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28 views

When to use the full and the conditional likelihood

In the context of estimating parameters of a time series model, we may consider either the full likelihood or the conditional likelihood. I was wondering 1) when does one use the full ...
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How to forecast the future values ( test data) by making use of the fitted ARIMA model [duplicate]

I am tried as follows But problem is all the fitted values are 39 Data : Test and Train data
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10 views

How do i Remove Differencing applied to a time Series, ARIMA model?

Am trying to forecast using time series method called ARIMA. I have followed steps to build a time series model displayed in the code below. My challenge is on (Merging Actual and Forecast in One ...
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12 views

How to correctly utilize seasonal dummies

Suppose I am fitting a model with Arima errors. If there are no seasonal effects the model might be : $Y_t = \beta_0 + \beta_1 X_t + \epsilon_t$, where $\epsilon$ is an Arima process Say the data is ...
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Difference between dlm and bsts

I'm working on a project which asks me to analysis the Facebook's stock price, and I have to do it the Bayesian way. This assignment doesn't have a particular goal and we are free to decide the what ...
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2answers
23 views

strucchange::breakpoints giving implausible results

I'm trying to detect the breakpoints in Facebook's stock price with strucchange::breakpoints. ...
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27 views

Model Selection with Time Series Analysis

I'm new to time series and would like some help to determine the parameters for my analysis. I have minutely data for a few months and this is how a random week looks like. I ran the Augmented ...
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16 views

Is there any difference in Trend Analysis and Time Series Analysis?

I want to compare prevalence of disease over time (years). I google "trend analysis" and first results where explaining "Time series analysis". Is there any difference in Trend Analysis and Time ...
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4 views

How to normalize data by mapping data points from one mixture of multivariate normal distributions to another mixture

How to normalize data by mapping data points from one mixture of multivariate normal distributions to another mixture Problem description I am trying to normalize multivariate time series data. The ...
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1answer
24 views

How to predict on test test by ARIMA in R [on hold]

I have analyzed the stock price for the forecasting stock price. I have split data into train and test set. I run ARIMA on the train set. But how can I forecast on the test set
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6 views

Diagnostics for first and second order (weak) stationarity

I am currently running a time series analysis which is mostly exploratory in nature. The data consist of a single sample of a univariate time series (equally spaced) and contains about 200 data points....
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9 views

Multiple & multi-step time series forecast training data with RNN

I have read a lot of discussion on how to do cross-validation on time series data (e.g. walk forward) but I failed to understand how to properly prepare the training data for multiple time series ...
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timeseries SARIMAX - how can i find the correct periodicity of traffic data? equation development [on hold]

i am a beginner in timeseries data analysis (using SPSS), i would like to understand how periodicity is optimally posed. i have traffic data per quarter of day (96) for almost a semester. traffic ...
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1answer
28 views

Exploring a loan data set when I'm interested in change in number of loans over time, not acceptance/rejection, defaulting, etc

This is a simple question but I am having difficulty wrapping my head around it. I have a dataset listing a bunch of individual loans. There are tens of variables describing aspects of the loans, mix ...
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1answer
47 views
+50

Do non-invertible MA models make sense?

While reviewing MA($q$) models, I came across these slides (Alonso and Garcia-Martos, 2012). The authors state that, while all MA processes are stationary, if they are not invertible you have "the ...
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1answer
12 views

Determining CausalImpact when there are 0s in the response variable

I am trying to run CausalImpact to look at the impact of an advertising campaign on customers who have shopped in a long time at the merchant in question. One of the variables I am using as a ...
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How to Interpret Confidence Intervals provided by CausalImpact

I am not sure how to interpret the confidence interval obtained when using the CausalImpact function in the CausalImpact R package. Is there a difference in interpreting the interval between the pre-...
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1answer
26 views

How to deal with unevenly spaced time data in time series linear regression

I have a dataset that has a number of instances that look like the following: ...
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6 views

Dynamic Time Warping - building a distance matrix with any distance that we want?

I am new to Dynamic Time Warping. I understand that we build a distance matrix and then try to find the cheapest path from starting to ending point. Typically euclidean distance is used to calculate ...
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Multiple Time series Forecasting Using LSTM in python

Assume I have a m dimensional input feature vector and I would like to perform multiple steps time series forecasting. I have about 500 files which each one is has 100 observations for example ...
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1answer
44 views

trend stationary with external regressors

Suppose I have two trend - stationary time series with strong correlation. In the case where there are no regressors, if a time series is trend-stationary, it becomes stationary by subtracting a ...
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1answer
20 views

Relationship between weak and covariance stationary

I have read that the definition of weak stationary is : $ Mean(t) = mean(t + \tau)\\ Cov(t_1,t_2) = cov(t_1-t_2,0)\\ E[|x(t)|^2] < \infty $ In this definition of weakly stationary, can the ...
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56 views

In an RNN, if the gradients don't vanish for long/distant terms, won't the derivative of the error be either divergent to infinity or oscillatory?

P.S. just cross posted here- https://datascience.stackexchange.com/questions/54322/in-an-rnn-if-the-gradients-dont-vanish-for-long-distant-terms-wont-the-deriv, as I still havne't got an answer from ...
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Conditional probability of n sequential time series points

I have hourly road traffic volume data. While I assume the volume data can be defined as normally distributed, there is some correlation. i.e. The traffic in the past hour may affect the traffic in ...
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17 views

What transformations can I use for time series with negative values?

Currently, I am working with different time series. Some of the series that I have can have all negative values or positive and negative values. When I have a time series with all positive values I ...
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Grouped Time Series Forecast when some of the nodes breakdown

I am attempting to do a grouped time series forecast in R using an ARIMA method at the base nodes. However at such a granular level, a few of nodes do not have enough data and so the auto.arima ...
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Decompose Time Serie [migrated]

I have a time series which represents the amount of a certain product sold throughout the year 2018. I am trying to decompose the time series but I get the following error ...
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Granger causality tests when series are measured at different frequencies

As a research exercise I should perform Granger causality tests on pairs of national series (one describing output gap and the other an index of consumers' confidence). However, I face a situation ...
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Bootstrap prediction intervals

How can I come up with bootstrap prediction intervals after fitting an ARIMA model? I also need help on bootstrapping as well as coming up with prediction intervals using r package. Anybody to assist?...
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Clustering timeseries subsequences (detection of modes)

I am working on a task that involves detecting different "clusters" of a timeseries signal. So basically I need to differentiate between "modes" (importantly, I do not know how many groups there will ...
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2answers
24 views

“Percentage” alternatives to MAPE

I'm aware of the problems of MAPE as a measurement, and particularly it's uselessness in the event of a time series where 0 is one of the many values of y. The downside to ditching MAPE in favour of ...
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6 views

Timeseries Forecast with log-normalized and differentiated data

i posted a similar, but more confusion question already. I have a weekly timeseries so far, which looks like this (pls ignore the red line): My original data is (e.g.): ...
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Granger Causality: Sum of errors vs. determinant

I have been measuring Granger Causality between pairs of vectors processes (i.e. 2 vectors consisting of multiple time-series variables). Most of the equations I find in references utilize a ...
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13 views

Detecting significant trend / non-stationarity in small sample time series

I am trying to detect whether there is a significant change in plankton size over time. As I understand, this is referred to as stationarity testing in time series analysis. Unfortunately, my time ...
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Why am I getting better MAPEs when running an ARIMA model on a non-stationary time series (vs. a stationary one)?

I've been using ARIMA modelling to predict the number of orders a business receives. I have data for 3 years, and the time series shows a strong (uneven) upward trend, with increasing variance over ...
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2answers
22 views

How to classify time series trends into 2 groups: “contain seasonality” and “doesn't contain seasonality”

I'm optimizing prediction model for time series data trends. Each trend may have seasonality effect or may not. I want to classify each trend into one of the following groups: "seasonality" or "no ...
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12 views

Dimension reduction for multivariate spatio-temporal data for hurricanes forecast

I have weather data for the 40 previous years and for each year I have information about the hurricane season (intensity, number of active days, casualties,...). My ultimate goal would be to forecast ...
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Hypothesis Testing : To Test the presence of Seasonality Given the Seasonal Indices

I was just looking at this tool by Professor Hossein Arsham to check for the presence of seasonality in the data using seasonal indices. https://home.ubalt.edu/ntsbarsh/Business-stat/otherapplets/...
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23 views

Timeseries with Log and differianted does not fit to predicted data [closed]

I am trying to built a data model with Knime, where I use functions in python for data wrangling and the metanodes (java/R) in Knime for forecasting. Untill now I discovered that I do not have ...
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1answer
13 views

Normalizing data before or after extracting time domain features

I have 100 time series (with 200 instances each) datasets each corresponding to a particular activity. I want to perform supervised classification for the activity. I want to use time domain (time-...
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2answers
44 views

Removing leading zeros from time series

Currently, I am working with a lot of time series data. A lot of my time series data have a lot of leading zeros. For example, ...
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1answer
22 views

What is the largest n root transformation I should consider for making a time series stationary?

Currently, I am working with multiple time series and not all of them are stationary. In order to make them stationary I am considering different transformations and checking the augmented dickey ...