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

learn more… | top users | synonyms

0
votes
0answers
45 views

SVM predictions of timeseries (forex) data are shifted

I am trying to build timeseries prediction SVM (regression variety) for forex data based on lagged close data. And I am using R. Please see the simple code below and resulting graph, using e1071 ...
2
votes
0answers
22 views

How to detect GARCH parameters must be updated?

I use a standard GARCH formulation: $$ \hat{\sigma}^2_{t+1} = \omega + \alpha r^2_t + \beta\hat{\sigma}^2_t $$ with $r_t = \sigma_t \varepsilon_t$ in order to have a daily estimate of volatility of ...
2
votes
0answers
36 views

ARIMAX or Dynamic Regression [closed]

What are the guidelines we should consider when choosing between ARIMAX and Dynamic regression(State Space model)?
1
vote
0answers
48 views

Predicting an ordered sequence of future events

Please advise on methods and models for prediction of future events sequenced in time based on some previous event history. I need to solve the following problem: Input data: a huge (750 GB) set of ...
1
vote
1answer
205 views

Time-series forecasting (in C#)

I'm developing an app in C# (WPF) that amongst other things, it makes a time-series based forecast of sales (4-5 months into the future). I'm an industrial engineer so I'm not pro in statistics nor in ...
1
vote
4answers
105 views

Time Series for each customer

Is it possible to create Time Series Analysis for each customer? Say if have 100 customers and I wanted to predict how much amount they are going to spend next. I have done the Time Series for the ...
0
votes
0answers
60 views

fitting a model for time series data

Folks, I am working on time series traffic data where the waiting times are indexed over time, with 288 observations for 24 hour time period (interval of 5 minutes). I am trying to cleanse the data, ...
0
votes
0answers
50 views

Mathematical representation of a seasonal ARIMA(1,0,0)(1,0,1)60

I'd like to represent the order of the Seasonal ARIMA(1,0,0)(1,0,1)60 model mathematically. Here is the equation I came up with so far: Eventually, i'd like to represent it as a "conventional" ...
1
vote
0answers
67 views

R forecast from STL

I want to understand how forecast from STL function in R works. So, I am not giving any reproducible code here. Below is the procedure that I worked on time series I used STL decomposition on my ...
0
votes
0answers
27 views

Forecasting sales for multiple departments using external factors

I have got the weekly sales information for various locations for about 3 years.It has got information for 157 weeks.Also,I have got the probable external factors affecting the sales.I want to ...
1
vote
1answer
95 views

How to understand this ACF

I have two time series. After calculating the ACF, they are like the plot below. Does anyone know the meaning of this ACF plot? I know it's non-stationary time series, but I don't know how the ...
0
votes
0answers
10 views

understanding chart of mean difference comparisons of groups over time

I am trying to understand a study which reports changes over time in the DSES (Daily Spiritual Experiences Scale).The table reports results for both the control group and the intervention group. The ...
2
votes
1answer
142 views

generate a time series comprising seasonal, trend and remainder components in R

I want to generate a time series comprising three components: a seasonal component, a trend component and a remainder component. Moreover, I want to be able to chnage the level of trend, seasonality ...
4
votes
3answers
83 views

forecast using arima models

I am trying to predict values using arima(0,1,1). After doing predict(mod,n.ahead=5) (in R) am getting the same value for all ...
2
votes
0answers
34 views

Zero p-value for Dickey-Fuller test

I feed sample time series data in statsmodels Dickey-Fuller test, and as a result I get exact 0 (zero) for p-value. I'm not sure how to intrepret that. I think that it doesn't imply stationarity as ...
1
vote
1answer
56 views

Estimation of VECM via ML and OLS

Take a vector error correction (VECM) model: $$\;\;\;\Delta y_t=\Pi y_{t-1}+\Gamma_1\Delta y_{t-1}+...+\Gamma_{p-1}\Delta y_{t-(p-1)}+\varepsilon_t$$ where $\Pi=\alpha \beta'$ and each row of ...
1
vote
1answer
54 views

Skewed posterior distribution on constrained parameter space for Bayesian inference of MCMC. Advice on what to do?

I am running a fully Bayesian MCMC procedure to estimate some time series models, and my model has a lot of parameter estimates. In particular, one of these parameters, $\phi$, is $\in [-1,1]$. The ...
0
votes
2answers
85 views

Why does the density function has product of variance and covariance for higher model order time series

In my previous question Density function for AR model, the density function of AR model has the covariance-variance matrix given as $\sigma^2 *V_p$. In multivariate Gaussian distribution, the pdf ...
1
vote
0answers
34 views

Holdout MAPEs in SAS PROC ARIMA and SAS Forecast Studio don't match

I have a Time Series (ARIMA) model in SAS, modelled using proc ARIMA which I am trying to replicate in SAS Forecasting Studio. What I see is that The parameter estimates in both are very similar ...
0
votes
1answer
22 views

Time series tracking queue optimization problem

In order to track prices of many different products from different sources, I must optimally schedule a group of trackers dedicated to price collection (ie. collect one price at a time for each ...
0
votes
0answers
17 views

AR(n) model with exponents

When we discuss a (time-series) model $AR(n) = \Sigma_{i=0}^n Y_{t-i} + \cdots + \epsilon_t$, we use $n$ to refer to the number of time steps back the autoregression includes. In other such models, we ...
1
vote
0answers
13 views

A question about Dickey-Fuller Unit-Root Test

I am reading Dickey-Fuller Unit-Root Test in Time Series Analysis with Applications in R by Cryer and Chan and have trouble understanding some discussion on equation (6.4.1). So they took this ...
1
vote
1answer
40 views

How to predict the time series data

I have no background of advanced stats. I am an engineer and I have the following data. I am representing it as a decent graph for better understanding. I want to forecast the collision for the next ...
2
votes
0answers
42 views

Time Series Decomposition - autocorrelation of error term

I would like to do time series decomposition, but the error term has a serial autocorrelation at the end and I am freaking out because I have really no idea what to do with that. How I did it? I ...
0
votes
0answers
29 views

Comparing influence of single independent variable on two dependent variables (time series)

Scenario description: Temperature has been measured at $k+2$ different depths in a borehole. Measurements of the temperature were taken once each hour over a period of about 3 months. So observed data ...
2
votes
1answer
52 views

Is time-series an appropriate method to model data sampled at widely irregular time intervals ?

I am relatively inexperienced with data analysis. My question: Is time-series an appropriate method to fit trends to data sampled at widely-irregular time intervals such as forests of different ages ...
2
votes
0answers
20 views

Augmented Dickey Fuller says it's okay. But is it really?

This is original sales data. The biggest spike occurs during winter holidays with a consequent customer inactivity around early January. My Augmented Dickey Fuller Unit Root test is quite significant ...
5
votes
3answers
166 views

How to interpret PCA on time-series data?

I am trying to understand the use of PCA in a recent journal article titled "Mapping brain activity at scale with cluster computing" Freeman et al., 2014 (free pdf available on the lab website). They ...
2
votes
2answers
151 views

Can I use the correlation between two variables when observations on each variable are autocorrelated?

I have two variables: urban areas protected areas. My observations are urban areas and protected areas in each year. But these observations are the cumulative ones, so observations in each ...
0
votes
0answers
52 views

One Step Ahead Forecasts Using Predict() in R

I just fit a model to a time series. I am now required to generate a 10-year extrapolation forecast of my model. My model includes a time term, a time^2 term, 12 seasonal dummies, and 4 lagged ...
3
votes
1answer
45 views

Using quadratic programming to fit a piecewise linear model plus seasonality

I am reading this paper on fitting an L1TF model to data using quadratic programming. Section 7.4 states how one could add seasonality to the model however it doesn't go very far into it. I am trying ...
1
vote
0answers
41 views

Selecting an appropriate VAR model

I would like to receive critical comments on an idea explained below. Suppose I have variables $x_1$ through $x_K$, and this is a time series setting. My aim is to forecast variable $x_1$. I know ...
1
vote
0answers
45 views

Stationarity ⇒ homoscedasticity? [duplicate]

If my data is stationary, can I also write that it is homoscedastic? Does stationarity imply homoscedasticity of the data?
0
votes
0answers
24 views

Basic questions about stochastic gradient descent / Robbins and Monro algorithm

I have a LOT of time series observations and I would like to estimate a simple AR(1) model $$ y_t =c+ \phi y_{t-1}+ \varepsilon_t \qquad \varepsilon_t \sim \text{N}(0, \sigma^{2}) $$ with parameters ...
7
votes
4answers
284 views

Analyzing up/down patterns in short time-series data

I have not worked very frequently with time-series data, so am looking for some pointers as to how best proceed with this particular question. Let's say I have the following data - graphed below: ...
3
votes
1answer
172 views

How to implement model in R?

i would like your help to implement this model in R or more explicity where yt = monthly mean values μi = mean value in month i, i = 1 . . . 12 . I1;t = Indicator series for month i of the ...
1
vote
0answers
26 views

How to compute the out-of-sample log-likelihood function?

I am doing some empirical work based on realized GARCH model, whose log likelihood function is given as $$ l(r,x;\theta) = -n\log(2\pi) -\sum_{t=1}^n \left[\log(h_t) + \frac{r_t^2}{h_t} + ...
1
vote
1answer
43 views

proving the asymptotic distribution of the mean

Let ${X_t} = \mu + \sum\limits_{j = - \infty }^{ + \infty } {{\psi _j}{\varepsilon _{t - j}}}$ with $\varepsilon$ is a white noise iid with variance $\sigma^2$ , $\sum\limits_{j = - \infty }^{ + ...
0
votes
0answers
24 views

what is the best method of smoothing time series for product share data?

I am having a share data for products presribed over period of time.The share is calculated like for 3 products each one 1/3 share and like that,where products may vary and hence their share. What is ...
0
votes
0answers
28 views

Finding genuine arrears and default arrears from rent payment patterns

I am currently working on some housing data - in particular analyzing the tenants' rent payment information and I am stuck on progressing with the following: I have to classify tenants based on their ...
1
vote
0answers
12 views

Coarse-resolution subsampling of time-series data

Suppose I have time series data with a very fine resolution, e.g. 100 datapoints per second. I want to report this data to some service that can only take data at 1 point per second. I need to do ...
0
votes
0answers
25 views

Techniques for comparing two windows of data in a time series

I'm working on a small independent project in R, trying to make my own (very crude) forecasting method. The general idea of the component that is giving me trouble is trying to compare two windows of ...
1
vote
0answers
43 views

Training/ Test Data with Time Series Model — Forecast with Training Model, or with Model based on Full Data?

Okay, I have a couple books on time series forecasting, but perhaps I need to read a couple more. Here's my question. You want to be able to validate a forecasting model. So you split the data into ...
0
votes
1answer
68 views

R times series — correct use of forecast() and accuracy() in forecast package

Cross-posting this from Stack Overflow, because it's a bit of a stats/ technology cross-over. I'm relatively new to R and the forecast package I believe authored by Rob Hyndman. I'm having trouble ...
1
vote
1answer
74 views

Analysis of residuals

For my master thesis I have implemented following forecasting models: naive (just to check) decomposition method exponential smoothing (single/double/holt-winters) SARIMA Now I need to do the ...
2
votes
0answers
30 views

Using A Time Series To “Scale” Another

I know the "average theoretical cost per impression" for Jan 13 - Dec 13. I have other monthly time series for "total # of impressions", "total # of clicks" and "total number of conversions" for Jan ...
0
votes
0answers
9 views

Given two time series where one is dependant on another how do we find the spectrum or acvf in order to then find the spectrum.?

$X(t) = aX(t-1) + Z(t)$ and $Y(t) = bY(t-1) - X(t)$ how do you find the spectrum if $a=b <1$ and if $a\neq b \neq 0$ and $a,b <1$?
1
vote
0answers
87 views

Hold out sample vs. cross validation for time series, and how to perform in R

I think out-of-sample validation testing for accuracy is essential in initially judging what time-series forecasts to use. In any case, I've been doing some reading on the two most common methods, ...
0
votes
0answers
20 views

Optimal length of observation window

I have a sample of N stocks, with time-series of daily returns. For each stock, I would like to compute the sample univariate variance of returns, using a rolling window. These variance will be used ...
0
votes
0answers
38 views

autocorrelation in evaluating time series forecasts

I'm having some trouble wrapping my head around whether using Holt-Winters ETS or an ARIMA model for forecasting sales figures (which are highly seasonal). I'm been using R and the Forecast package ...