Questions tagged [time-series]
Time series are data observed over time (either in continuous time or at discrete time periods).
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Does the difference in difference techniques assumes the dependent variable has a normal distribution?
I'm trying to estimate the effect of an event. The data I have is number of sales in 7 different periods of time (3 periods before the event, the period that the event happens and 3 periods after the ...
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31 views
Error while fitting data in auto.arima - R
I am running auto.arima for forecasting time series data and getting the following error:
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Why is arima in R one time step off?
I've recently noticed an odd behavior in a few timeseries methods. Let's fit an arima model (ar1) to the annual subspots data
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How to handle partial observations of the variable of interest when training a time series model?
I have the following time series data:
$\{ t_i, X_i, Y_i \}$
where $i$ is the index, $t_i$ is the timestamp, $X_i$ the measured value of the external variable and $Y_{i}$ the value of the variable ...
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How to approach machine learning time specific data; which months of usage to use?
I am relatively new to the data science area and just have a question about how to approach a time specific machine learning problem. Just as an FYI I am currently using a random forest classifier for ...
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For autoregressive time series modeling, does the AR(p) regressors have to be in order despite insignificance?
I am trying to fit a time series model using data of auto sales (DAUTONSA from FRED) and noticed that there is evidence of serial correlation. I’ve tried fitting a model with 4 lags but noticed that ...
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How to transform my data appropriately? [on hold]
When I did time series analysis and plot the time series plot, the plot shows non-constant variance. How can I process it if the plot like this.
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Fractional un-differencing of time-series
I have time series y that has long-memory 0 < d < 1.
I have differenced y with order d, resulting into y'.
I would like to now restore y' back into y.
Do you guys know how to do that?
P.S.
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Linear models and multiple comparisons on time-series data
I have conducted a study on subjects corresponding to two groups G1 and G2. Each subject in the group was tested under two conditions C1 and C2. The testing under each condition was done across a ...
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Algorithm for producing a Moving Average (as in ARIMA) model
I have a time series $X_t$ and I want to produce an ARMA forecast (without using any automated packages - the purpose of my project is to understand how those work).
So far, I have the AR(p) part ...
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1answer
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Dealing with missing month values in dataset and plotting a time series
So i currently have a dataset in R:
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1answer
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Why the dependent variable as a time series in GAM should be log transformed?
I found in Section 3.1 of this paper (see ), the author used log(y) due to "the context of a concentration time series". A book (Hastie and Tibshirani, 1990) is cited, however I cannot get this book. ...
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Forecasting with time series with different interval
I am trying to forecast monthly inflation rates using weekly percentage change of commodities prices. Is there a way to do this without losing info? or can I like get the predictor's moving average ...
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How to simulate average data based on time series model? [on hold]
I am using R Studio. I have a dataset of electric load for a building taken at 5 minute intervals for an entire year. I generated a time series vector using a daily frequency and have trained an ARIMA ...
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1answer
13 views
How to calculate correlation for 2 time series where the tick times do not line up
I'm looking to compute intraday correlation for 2 tick by tick intraday commodity price time series.
The tick times do not line up across the 2 time series. I'm thinking of converting the time ...
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How to forecast a time-series with a dynamic time unit?
I'm working on a forecasting problem, and I'm not sure if the data requires any transformation because the unit of time is dynamic.
I'm working with an education data set where I have data on ...
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Interrupted time series analysis with ARIMA in R [on hold]
I would like to ask.
I have a time series of sales. I would like to evaluate the impact of it. the code should look like this
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1answer
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Interpreting Results of Time Series Decomposition
Suppose, after ACF and PCF investigation, I see my data has strong seasonality. I do both additive and multiplicative time series decomposition, just to check it results.
I am not interested in ...
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How do you differentiate between white noise in the data to analyse significant trends? [on hold]
How do you differentiate between white noise in the data to analyse significant trends?
I'm analyzing sales data over time which has a lot of noise.
I can think of performing regression to check for ...
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Univariate Time Series Ramp Detection
Given a univariate time series, what are common / best practice ways to detect "ramp up" or "ramp down" in a time series over an extended period of time?
For example, given a daily time series of an ...
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Sufficient Condition Stationarity AR(2) process
Given the following AR(2) process:
$y_{t} = \phi_{1}y_{t-1} + \phi_{2}y_{t-2} + u_{t}$
I need to prove that the sufficient condition for this process to be stationary is $\phi_{1} + \phi_{2} < 1$....
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Understanding epoch, batch size, accuracy ,performance gain in lstm forecasting model
I am new to machine learning and lstm. I am referring this link LSTM for multistep forecasting for Encoder-Decoder LSTM Model With Multivariate Input
section.
Here ...
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1answer
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How to interpret these ACF values and understanding concept - Very confused please help?
I am relatively new to statistical forecasting, I understand how to analyse the ACF plot to identify certain components such as seasonality, trend etc.
However I am having trouble understanding the ...
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Durbin-Watson test and coefficient significance yield different conclusions?
I'm not sure where my confusion is stemming from, but it seems that equivalent tests (Durbin Watson, and a simple significance test) for serial correlation in the errors (of lag 1) sometimes yield ...
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1answer
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Comparing volatility of house prices over time
I am trying to compare the volatility of housing markets over time across different countries. I want to be able to determine whether for instance average annual house prices are more volatile in the ...
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1answer
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Forecasting daily data with annual seasonality
i have been trying to do the forecasting model. My data has daily value and there is annual seasonality and probably weekly.
My question is which model will be the best. I have tried with SARiMA but i ...
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1answer
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Forecasting with AR(1) and pseudo out-of-sample using R
I'm trying to do Pseudo out-of-sample forecasting using R. And, I also have the following initial data (gdp)
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9 views
Estimating arma(0,0) in eviews [on hold]
I am trying to pick the best ARMA model based on the models SIC value. I want to check the same for an ARMA(0,0) model, would the eviews command to estimate the model be “ ls y c”? To estimate an ARMA(...
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VAR or VECM model- 4 variables
I have a model consisting 4 variables, two of which are stationary at level and two that are I1 stationary. There seems to be a cointegration relationship between the two I1 variables after running ...
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Lomb-Scargle plot cutoff point
With the Fourier transform we know that we can plot up to the Nyquist frequency for meaningful results (avoid aliases). I assume there's something analogous for Lomb-Scargle. If I have a week's ...
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3answers
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Modelling count data with time-series structure and predictors
I am doing an analysis of sales-data over a period of time (i.e. over a few years). Those sales-data are also dependent on some predictive variables (i.e. holiday, weekend, weather,...). The daily ...
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3answers
298 views
Does lack of seasonality imply random time series?
Some techniques for time series analysis (prediction) require that the time series not have seasonality. It seems that without seasonality, a time series is essentially random, in which case ...
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finding regression coefficients and deviation with autocorrellated outlaws
I try to make regression analyses to vector of average month C02 concentration in the air.
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time series generator in loop [closed]
I have a problem with very big data set. I need to generate n time series with unique ID. It's means at the first step after analyzes I have a data set which ID list - as factor ...
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1answer
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Time Series Forecast [closed]
I am trying to forecast sales qty for each product ID for the next 52 weeks on the basis of 10 weeks data.
For more clarification here I have attached the image of Data set so that you could ...
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Why does my Gaussian Process Regression on multivariate time-series data work well on training, but predicts only the mean for test?
My data is an hourly multivariate time-series consisting a temperature ($y_t$) and 7 other weather features $\mathbf{x_t}$ (e.g pressure, humidity...).
$\mathbf{W} = \{(\mathbf{x}_1,y_1),(\mathbf{x}...
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Time series novel
I've exhaustively attempted to find a proper way to analyse a dataset. Despite finding several piece of information of what could be done, I kindly ask for suggestions of could be done, mainly in R.
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Stationary data, ADF test and interpretation, first difference ,OLS regression?
I am currently working my master's thesis and am studying bitcoin price determinants using OLS regression. My statistics level is quite beginner level.
I have gave stationary tests a go however I am ...
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Correlation of Aggregate Data on a monthly to quarterly basis
I am working with a data set of 42 countries of monthly migration. I want to extract factors using PCA, and find non stationary errors of my model, so I am working in first difference. However there ...
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1answer
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Interrupted time series analysis of data with deterministic trend and seasonality
I am trying to evaluate the impact of an intervention on a selected outcome variable using interrupted time series data. I have aggregated a five-year data into monthly values to create a data-set 0f ...
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Using longitudinal income data to predict cross-sectional outcome measure
I have the following data:
income data measured yearly from 2004 to 2011 in households occupied by adolescents
a single variable denoting households where parents have divorced (divorced vs non-...
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1answer
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Cross validation for time series classification
Relying on the documentation provided by scikit-learn (https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation-of-time-series-data), the TimeSeriesSplit method is implemented ...
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Is there any benefit of using GLS when the regressors are identical
I am reading Greene, Econometric Analysis, 7th Addition,
I am seeking a point of clarrification.
"The case of identical regressors is quite common [think a VAR mode].... In this special case, ...
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3answers
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ACF-PACF could be the diagnostic model of seasonality?
If seasonality is not shown in the acf-pacf plot, can I conclude that the seasonality does not exist? Or Can I say that the seasonality exists based on the monthly incidence data although there is no ...
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Stationary Series
By looking to the graph, is the following series stationary?
kpss.test(r, null = "Trend")
KPSS Test for Trend Stationarity
data: r
KPSS Trend = 0....
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1answer
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K in Fourier series - How to find value of K to use it in ARIMA?
I am using the forecast library for doing some time series forecasting. I need to forecast number of sold items. I am planning to add holidays as xreg in auto.arima. The holiday will be a 0/1 list, ...
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On Using the Durbin Levinson Algorithm
Let $X_t-\phi X_{t-1}=z_t+\theta Z_{t-1}$ be an ARMA(1,1) model.
GIven data $X_1=x_1, X_2=x_2$ I want to estimate $\hat{X}_3$ using the Durbin Levinson Algorithm.
The general expression is:
$\hat{...
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Autocorrelation of stochastic process with python
So I am trying to simulate a SDE and find the corresponding correlation function.
The equation is $$x=\beta +\sqrt{2D} \xi(t)$$ where $\xi(t)$ is a white noise term.
After solving it using Euler-...
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1answer
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Understanding the infinite sum of random variables
I am doing a course on time series analysis, and am struggling with this definition:
We call a weakly stationary process $\{X_t\}$ invertible with respect to a
white noise $\{\epsilon_t\}$ if ...
