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

learn more… | top users | synonyms

0
votes
1answer
188 views

Seasonal naive in forecast package with multi-step prediction

I am working on predicting a time series of daily data for one month that looks like this: As can be seen, the time-series has a weekly seasonality. I am trying to predict the next week's data (...
1
vote
0answers
19 views

Time series with 24 yearly data points - advice needed

I have a dataset containing the prevalence rate of Malaria in Botswana, starting in 1990 and ending in 2014. My task is to verify whether these data can be used in order to make predictions on the ...
0
votes
0answers
11 views

what can I conclude from the following Granger causality test?

I have two monthly time series: one for house prices expressed in annual change growth rates( [ln(Xt) - ln(Xt-12)]- [ln(Xt-1) - ln(Xt-1-12)] ) and the other simply in growth rates: ln(Xt) - ln(Xt-1): ...
0
votes
0answers
11 views

State-of-the-art methods for forecasting time series array

Suppose I have a set of measurements taken at regular intervals, and I want to predict future values of one of those measurements. There are relationships between the variables being measured. For ...
1
vote
0answers
10 views

Time series cross-validation, calculate RMSE for different forecasting horizions

Following Rob J. Hyndman suggestions on how to do cross validation for time series ( http://robjhyndman.com/hyndsight/tscvexample/ ), I modified his original code to evaluate how RMSE (not MAE) ...
2
votes
1answer
2k views

Fit a VAR model with R

I have a bivariate time series z_t where z_1t is the change in monthly US treasury bills (maturity 3 months) and ...
0
votes
0answers
17 views

Generating smooth time series

I want to create a smooth time series that follows a non-linear simple model, for example something like this: $Y_{t} = 0.5(Y_{t-1})^{3/2} + \epsilon_{t}$. The important part to me is to have $Y_{t-...
0
votes
1answer
21 views

Simulating a Stochastic Integral of OU process

The stochastic integral I want to simulate is $$\int_{0}^{1}J_c(s)dJ_c(s)$$ where $J_c(s) = \int_{0}^{s}e^{-c(s-r)}dB(r)$, is an OU process. I simulate the data using Matlab and the sample codes are ...
2
votes
1answer
307 views

Weighting time series coefficients using model's likelihood

I have a question regarding to time series forecasting. In particular I've been working with a Bayesian approach, but I think the question is independent from that. I have several time series which ...
41
votes
9answers
6k views
+100

What is wrong with extrapolation?

I remember sitting in stats courses as an undergrad hearing about why extrapolation was a bad idea. Furthermore, there are a variety of sources online which comment on this. There's also a mention of ...
3
votes
0answers
41 views

Perceptron trained on time series always predicting the same answer

Using the model from theano's tutorial, I'm training a 3-layers perceptron with log returns over a very large dataset (~55,000 points). The output's layer contains two neurons, one for each of the ...
5
votes
4answers
110 views

What is the best way to detect repetition in xyz data for purposes of splitting data?

I'll use this picture to explain What I want to do is define some patterns as trained patterns. Then given data I want to be able to determine if the pattern exists in the dataset, and if it does ...
0
votes
0answers
8 views

changepoint detection and its analysis

I applied changepoint detection provided by ecp package against a given time series. The time series plot marked with identified change points is shown as follows. ...
0
votes
0answers
14 views

Raster time series smoothing

I'm searching for an R package to raster time series smoothing. Currently, I'm using an approach like this one (using the equation suggested by Hamunyella et al., 2013) ...
2
votes
1answer
480 views

How to apply an AR(MA) model to a prewhitened signal?

I have two (vehicle velocity) signals that should consist of similar "latent" drivers, but have different autocorrelation structures. The driver-signals are quite nasty statistically, so I'm not ...
1
vote
2answers
26 views

Model/explain a time series as a function of other time series - R

I have six time series, all made of daily historical data. All of them cover the same period and the same days, they are all about 2700 days long. I want to explain one of the time series as a ...
2
votes
1answer
235 views

How to estimate model where instrument is correlated with dependent variable

I have the following problem: I would like to estimate the effect of price variation caused by uncertainty on an outcome variable. P is my price, X is the variable measuring uncertainty and Y is the ...
0
votes
0answers
34 views

How to model really rare events in a single time-series

The event of interest happened only 5 times in the last 4 years. My independent variables are the number of results returned by Google Search for specific keywords over time (and per domain of each ...
0
votes
0answers
11 views

Year effects in fixed effects regression

Could explain me how the interpretation of the coefficients changes when I have not only group effects (one way fixed effect model) but also time/year effects (two way year effects)? I am studying ...
2
votes
1answer
297 views

Multivariate time series model evaluation with conditional moments

Consider multivariate time series models that estimate potentially time-varying conditional means, variances, and correlations (one type of model might be a VAR(p)+Garch(1,1)+DCC Gaussian Copula model)...
0
votes
0answers
18 views

Demand Forecasting Models

I want to forecast demand of various products using time series data of 2 years (using loops on products in R), frequency is daily and demand is to be forecasted for next 90 days I have used the ...
2
votes
0answers
23 views

Parameter estimation for ARIMA around a complicated deterministic mean

Currently, I am trying to fit a time series to the following model: $$ (1-\phi_1B-...-\phi_3B^3)(y_t-\mu_t)=\varepsilon_t(1-\theta_1B-...-\theta_3B^3), $$ where $t=1,\dotsc,n$ and $y_t-\mu_t$ is ...
0
votes
0answers
8 views

Machine learning implementation in Ultrasonic sensor data [on hold]

I am using an ultrasonic sensor to detect liquid level(milk) in a container(silo). Length varies to 25 cm to 8 m. I am looking for ideas on machine learning implementation of the data received. I ...
1
vote
1answer
186 views

Assigning more weight to more recent observations in regression

How do I assign more weight to more recent observations in R? I assume this as a commonly asked question or desire but I have a hard time figuring out exactly how to implement this. I have tried to ...
2
votes
2answers
2k views

How to extract long run and short run coefficients from ARDL (UECM) estimates?

I have estimated ARDL(UECM) in eviews but I dont know how to specify or extract the long run an short run estimates/coefficienst? what is the standard procedure to do so?
1
vote
2answers
269 views

Periodogram vs. spectral density diagram of a time series

Could someone explain to me the difference between a periodogram and spectral density diagram? The first diagram is produced with this block of code: ...
1
vote
1answer
271 views
1
vote
0answers
14 views

Choosing between ets or arima model

I have a time series and two models to choose from: ETS and ARIMA. I have used the MAE to select a model. But when forecasting the time series and comparing the models, I don't know which model ...
0
votes
0answers
11 views

Time Series forecasting using Kalman Filter (in Matlab) [on hold]

I have searched online for an example of time series forecasting using a Kalman filter. Specifically, I would like to forecast a stock index, e.g. the Dow Jones, using the filter and do this in Matlab....
0
votes
0answers
13 views

Repeated measures MANOVA for testing difference among multivariate time series?

I measured behavioural multivariate time series (3 variables) from 7 dyads performing an experimental task. For each dyad, I have a multivariate time series (that is, I don't have data from each ...
1
vote
0answers
30 views

Studying fluctuations in time series

I have some time series to analyze. Given the domain the data is coming from - Time series is supposed to have some fluctuations. A regular periodicity might not be present at all in some cases. ...
1
vote
0answers
24 views

Dimensionality reduction for multivariate time series

I have a data set including 25 variables $(x_{1,t},\dotsc,x_{25,t})$ at each time $t$ and all of this group is repeated through time. I want to explore the relationship between these variables through ...
1
vote
2answers
344 views

Neural network for time series forecasting- Single input Single output Theoretical proof needed

I am doing time series forecasting using neural networks. I have 2 approaches: Forecasting in a auto regressive manner i.e based on time series lags as shown below: ...
0
votes
1answer
21 views

Applying bayesian inference to a time based problem

I have a N*M matrix with N customers and M products. Each row of this matrix is a M dimensional vector like [1 3 4 1 5 ....] where each value represents how many times that customer has chosen this ...
1
vote
0answers
13 views

Time Series: How to Deal With Differences In Public Holidays?

I am conducting analysis on the relationship between stock markets in regions with differences in the date of public holidays. That is, in some countries, Friday and Saturday are public holidays while ...
2
votes
2answers
229 views

Determining odd time series

I have a number of time series which are derived of same underlying data. However, the data in each comes from a different source, so they may be slightly lagged or differently enriched but ...
1
vote
1answer
259 views

Time series modeling of choppy data

I'm trying to model 10 years of monthly time series data that is very choppy, and overall it has an upward trend. At first glance it looks like a strong seasonal series, however the test results ...
0
votes
0answers
3 views

Stacking time series data vertically [migrated]

I am struggling with manipulation of time series data. The dataset has first Column containing information about time points of data collection, 2nd column onwards contains data from different studies....
9
votes
1answer
121 views
+50

Mathematical and statistical prerequisites to understand particle filters?

I am currently trying to understand particle filters and their possible uses in finance and I'm struggling quite a bit. What are the mathematical and statistical prerequisites I should revisit (coming ...
0
votes
0answers
12 views

How to calculate first order autocovariance in AR(1) process?

For a process describing dividend yield $x_t$, it is assumed to follow a first-order autoregressive process: $ x_t=\delta +\phi x_{t-1} + \eta_t $ where $|\phi|<1$ and $\eta \sim \mathcal{N}(0,\...
1
vote
1answer
43 views

RNN learning sine waves of different frequencies

As a warm up with recurrent neural networks, I'm trying to predict a sine wave from another sine wave of another frequency. My model is a simple RNN, its forward pass can be expressed as follow: $$ \...
1
vote
0answers
148 views

Appropriate time series model with multiple observations at each time point?

Data description: My dataset (~2 millon rows) includes cattle price records from weekly cattle markets during the period Jan 2008-Dec 2014. For each animal, the following information before the sell ...
0
votes
0answers
11 views

Given Three Time Series Estimate a Fourth

I have three time series $Y^{1,2,3}$ that I expect to vary according to some parameter that modulates the amplitude for a specific window. How can I estimate a fourth time-series based on their ...
3
votes
0answers
33 views

K-fold cross-validation for time series with dynamic target variable (Scikit)

I would like to do a K-fold cross-validation on time series data (market data) with a two class classification target. My test folds must be forward looking and of a fixed size ...
0
votes
1answer
25 views

What imputation methods can be used for missing not at random covariate values in a survival analysis?

I'm new to survival analysis and trying to understand how to use it properly. My dataset is a time series dataset where most dependent variable values are available, 2 dependent variable values are ...
0
votes
0answers
13 views

Strong fluctuations in level component after TBATS

I have 2 time series sampled at a weekly level spanning a period from the start of 2010 until the present. Initially I had used a TBATS model with the frequency of the time series set to ...
1
vote
1answer
20 views

Example of 2 series correlated but not cointegrated and vice versa

I am studying the time series and only kind of understand correlation vs cointegration. Can someone provide an example of two series that are correlated but not cointegrated, and two that are ...
1
vote
1answer
137 views

How to graph throughput

UPDATE 2: maybe a simple way to state it: for web application performance data consisting on: start timestamp | end timestamp | response time I'd like to compute a non-aggregated data set consisting ...
0
votes
1answer
21 views

Timeseries with binary regressors

I'm trying to identify impact of some causal events on a given timeseries. However, the trouble is I only know whether the event occurred or not (binary). What kind of techniques can I use to create ...