Questions tagged [time-series]
Time series are data observed over time (either in continuous time or at discrete time periods).
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How do I handle basic time series prediction?
I have data like:
may 2019 value=74 cost centre=c1 and I want it to predict values for cost centres. The data is monthly and I only have a few years worth (max 48 ...
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Using weather forecasts as exogenous data for timeseries forecasting
I'm using Facebook Prophet as a forecasting model and I want to use weather data (temp for example) as additional regressor (exogenous data or external variable).
Additional regressors are integrated ...
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How to convert multiple rows into single rows in Python for prediction for next t days?
I have time-series data. I have taken the dataset from Kaggle [https://www.kaggle.com/code/kp4920/s-p-500-stock-data-time-series-analysis/comments]. So, how can I bring multiple rows into single rows ...
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Variations of difference-in-difference method
I have data from a group of patients and for each patient I have two time series A and B (continuous quantity) sampled on a weekly basis. My hypothesis is that quantity B has a 1-week lagged effect on ...
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How does the dynamic regression component from TBATS differ from the one in SARIMAX?
Both TBATS and SARIMAX support the modeling of long-term seasonality by using dynamic regression. Dynamic regression in this case involves the use of sin and cos terms to model seasonal components.
In ...
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Seasonal and trend adjustment for irregularly spaced time series
I know of different methods that exist to remove seasonality and trend in the data to make it stationary. However, that exists only for regular time series; that is, a series that follows a fixed ...
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Difference between cross validation vs model accuracy measures
I have a time series ARIMA model and I want to validate the accuracy my prediction. But I dont understand the difference of using cross validation vs model accuracy measures such as MAPE, MAE, MSE and ...
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Time series - ARIMA Model gives bad result of prediction
I don't understand why I get poor prediction results with ARIMA Model
Here is my program and my results:
for the dataset I am using a file (which represents CPU traces of one VM)that contains 288 ...
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Identify predictors for a symptom in a time series
I have a dataset of time series. The analogy for each series is a medical history (2-3 years) of a patient visiting a clinic. It consists of dates and symptoms per visit:
There are few thousands of ...
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States in RNN: what if they are measured
A simple RNN will take the output and feed it back to a state variable. The next output is then the function of input and state.
Now imagine I want to use the "state" variable in RNN as an ...
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Seasonality component and stationarity of a time series
I am not able to understand the seasonality pattern in the below plot. For me, the series has obvious seasonality (except for 2014 year) where levels tend to increase in the early months of the year ...
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Time series model in production - Re-train on the fly as as batch process?
Let's say I've a time series of phone calls per day over the last three years. I could train a model using exponential smoothing (e.g. HoltWinters) for predicting the future amount of phone calls per ...
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Comparing intervention/control group
I am planning to conduct a study which is based on an RCT design.
I am intending to conduct an intervention. During this intervention, participants and control group enter some medical data on daily ...
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Missing Prediction Intervals by R package autoTS
I'm using autoTS R package to fit automatic time series estimation and prediction to a very very large number of time series.
One of the outputs of the prediction function (my.predictions()) is a 95% ...
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R package for order selection of a vector ARIMA(p,d,q) for multivariate time series
I'm currently working on a project that requires dealing with multivariate time series. What I would like to fit to my dataset is a VARIMA(p,d,q) . I'd like to proceed using the Box-Jenkins procedure, ...
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How to derive variance of an AR(1) model? [closed]
I am trying to derive the variance of $Y_t$ and $X_t$. Please help.
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Do the variables used in computing the mean and variance have to be i.i.d.?
For example if you have a theoretical server that processes incoming requests where in this case there are so many requests that the callback delays depend upon each other, can you just take the ...
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Can you generate correlated ARMA time series with different lag orders?
Is it possible to generate correlated ARMA time series when the ARMA models are different? For example, can you generate two time series which are correlated when one is an AR(5) model and one is an ...
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Interrupted Time Series Design with multiple groups
More of a conceptual question
The standard ITS design is pretty simple, regress:
Y ~ Time + Treatment_Dummy + Time_Since_Treatment
However, what if the scenario was ...
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Is stationarity of variables neccessary condition for Bayesian VAR?
I am trying to run a BVAR on 5 variables. Four out of five are non-stationary. So shall I do the first difference of the non-stationarity variables or take them in level for running the BVAR?
And what ...
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How to calculate harmonic equations from a complex wave
I’m working through an online example that explains spectral analysis in R. Let’s say you have a complex wave:
...
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Structural or sensitivity analysis of multivariate time series with multiple subjects
Sorry if this isn't explained in the best way. I have very basic knowledge of time series analysis so my question may sound very simplistic or might be missing the big picture of this type of analysis....
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Time series data with seasonality using model VAR and VARMA [duplicate]
I have a time series with seasonal economic data, and other time series to see if this variable help predict my time series, but the VAR model is not a model for seasonality.
What options do I have ...
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Concatenation or separate channels for a CNN
let's say I am classifying time series data from multiple channels in a biomedical setup (e.g. 12 lead ECG).
I have been reading this paper on a CNN-based (ResNet) architecture for assesing the ...
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Are stacked meta models for time series forecasting still considered forecasters?
I am working on building a meta model that is based on the principle of stacked generalisation (1). In a nutshell, this method works by using building a meta model based on the predictions of various ...
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Efficient storage of functional data
I have access to a sample (size $N$) of functional data. Each observation corresponds to $C$ functions. Each function $f_{n,c}$ is represented by $T_n$ points for $1\geq n \geq N, 1\geq c \geq C$. All ...
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Difference K-fold versus Blocked Cross-Validation?
In the paper "Evaluating time series forecasting models: an empirical study
on performance estimation methods" by Cerqueira et al (2020), they mention k-fold cross-validation. Which they ...
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Understanding types of LSTM and their use cases
I'm currently considering to use RNN/LSTM for a predictive modelling project that involves time-variant points. From looking at the following types of LSTM/RNN (in the picture below), I want to try ...
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ACF and PACF Plot
I am a first year stat student. We are tasked to create a SARIMA model from trial and error using ACF and PACF plot. Now here is my generated plot:
Now I am trying to understand the plot but I don't ...
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What is the difference between regression and state-space models?
I would like to know the differences between a regression model with autocorrelated errors and state space models (time series). When should each be used?
According to this lecture, regression (linear ...
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Arima model always predicts the same value in python implementation, R's Auto Arima however gives me a well function model. How to replicate?
I have a series that looks like
. (https://gyazo.com/8a460fed032c8989b93cf26d8820e431)
It shows very strong auto correlation ![AC]
(https://gyazo.com/4acd9b9bd32c70509bde1b8b874d6e33).
I have ...
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Alternatives to linear model regression
Let's say I have a univariate linear regression model LMR in which ...
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Practical correlation metric for a large number of vectors
I am dealing with a timeseries consisting of input flow sampled every 5 minutes over 441 days. My aim is to find any possible correlation from data coming from:
The same day of the week
The same ...
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Does the Discrete Fourier Transform have a factor of $n^{-1}$ or $n^{-1/2}$? [closed]
While studying, I have encountered two formulations of the Discrete Fourier Transform, and I am not sure whether the choice of having a factor of $n^{-1}$ or $n^{-1/2}$ in the DFT is arbitrary. The ...
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Modified Structural Break Test
Consider a simple AR(1) model
$$y_t=c+\lambda y_{t-1}+\epsilon_t$$
For example using the so called Andrews supremum statistic, I can test for a single structural break in $\lambda$ at an unspecified ...
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In a time series $x_t, x_{t-1},...,$, why is $E[x_t|x_t, x_{t-1},...]= x_t$?
Sorry if obvious but in a time series $x_t, x_{t-1},...,$, why is $E[x_t|x_t, x_{t-1},...]= x_t$?
I don't really get what the random variable $x_t|x_t, x_{t-1},...$ represents?
What I find ...
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statsmodels.adfuller weird behaviour of usedlag value
I'm learning about time-series analysis and have two series on which I'm performing an Augmented Dickey-Fuller test in order to check for stationarity.
I'm trying to understand why I get very ...
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How to select a time series model based on this ACF?
I think that I am missing some concepts because I am not able to model adequately this series.
It is a times series over 12 years.
I tried with multiple AR and MA models, nevertheless, I can't model ...
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Conflicting ACF/PACF after first-difference
I have yearly data. When I do a Dickey-Fuller test it gives me insignificant results, indicating that the series are non-stationary.
After differencing them the DFT tells me they are now significant ...
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How can I adjust cumulative entropy of moose observations?
I have downloaded the dates of every moose (Alces alces) observation worldwide on iNaturalist. This amounts to about $2 \cdot 10^4$ observations at present. I excluded one observation that was either ...
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Estimation of standard error in observables generated from time series data
Imagine that I have time series data which are time-correlated, non-scalar, and of unknown, but identical distribution
From this time series I have a function that takes an subset of X as input to ...
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Slowly decaying ACF plot = indicative of trend or AR(1) process?
I am required to interpret a suitable ARIMA model for the above ACF and PACF plot. My first instinct from the slowly decaying ACF plot and the sharp drop after lag 1 of the PACF tells me this is an AR(...
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When do ARMA models fail?
I have just started learning about Autoregressive–moving-average model (ARMA). On the Wiki page, it has been mentioned that:
ARMA is appropriate when a system is a function of a series of
unobserved ...
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Statistical test for forecast performance over multiple runs
Lets say I have a time series, create a training and test set, and I want to compare the predictive accuracy of two models, by measuring e.g. the mean absolute error (MAE) over the test set. I know ...
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How to forecast sales for entire current month taking into account sales from half of month?
Good afternoon!
I want to forecast sales for current month. Since I already know sales for two weeks of current month, I want to incorporate this information into forecast for the whole current month, ...
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Over forecasting when using historical data during the pandemic period?
I am new to the forecasting domain. I am dealing with a forecasting task where we had a very high abnormal demand for our products during the pandemic. However, in recent months, the demand trend is ...
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Inclusion of year and seasons as variable for regression with non-stationary response?
The common knowledge is that OLS only makes sense if both the response and explanatory variables are stationary (ignoring exceptions like cointegration), as otherwise, there could be effects of ...
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how to measure the effect of a recurring event?
I have multiple cities with data on theatre visitors and an event as the mentioning of the theatre in the local news.
I want to estimate whether the event of a mentioning lead to more visitors for the ...
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Why does my ARIMA predictions on monthly data form a straight line?
For short detail, the goal was to forecast using 51 monthly observations of KPI of project implementations which I aggregated by sum from 463 observations from about 4 years of data (May 2017 to July ...
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Autocorrelation function of Arima(1,1,0)
I have ARIMA(1,1,0) model:
(1-ΦB)(1-B)X_t = ε_t
Where ε_t ~ WN(0,σ^2), |Φ|<1, DX_0 < ∞.
How do I calculate the autocorrelation function?
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