# Tagged Questions

9 views

### Time series prediction: visualising path uncertainty region

I am predicting a time series' future evolution and am evaluating the path uncertainty using bootstrapping. Is there a good way to visualise the uncertainty that goes beyond simply plotting a pair of ...
3 views

### ARDL, Lag Terms and Singularity

I am interested in fitting an ARDL model that has 4 lags for each explanatory variable. However, when I fitting the model in R. R says that coefficients are not defined because of singularities. Is ...
24 views

### Heteroscedastic ARIMA [on hold]

Is there an implementation of ARIMA models in R which allows for heteroscedastic residuals between ARIMA model and original time series?
38 views

### Time series modeling with R on weekly data

I am trying to do time series modeling and forecasting using R based on weekly data like below - ...
67 views

### How do I interpret regression coefficients with autocorrelated residuals?

I am building a regression model of time series data in R, where my primary interest is the coefficients of the independent variables. The data exhibit strong seasonality with a trend. The model ...
30 views

### Time series of daily mortality, pollution and interactions

I would like to estimate association between daily death and daily pollution. For that I used the model mod1 found below to estimate the overall association of exposure and outcome. I am interested ...
69 views

### help on how to include term $\exp(β_t)/(1+\exp(β_t))$ in AR(2) model

I am trying to include a term in an AR(2) model: $$Y_t=\left( a_0+a_1 \frac{\exp(\beta_t)}{1+\exp(\beta_t)}\right)Y_{t-1}+bY_{t-2}+\delta\epsilon_t$$ Can anyone please help me with this? I don't seem ...
48 views

### Forecasting time series with R forecast package [on hold]

I'm relatively new to R programming, but I've been reading your blogs and posts in order to get up-to-date with the forecast package. However, I have been ...
34 views

### ar() time series function in R, manually checking the residuals/predicted values

I am using the ar() function to fit an AR model to some data, and this object will return the in sample residuals. I also know the syntax for how to get the corresponding predicted values, but I want ...
24 views

### fpp forecasting using AWS ubuntu

Is the package fpp (or any of its previous incarnations like forecast) supported in Ubuntu 12.04 using AWS? It is the only package that R downloads but when you load the library it throws an error. ...
130 views

### R detect increasing/decreasing trend of time series

I have lots of time series with periods: day, week or month. With stl() function or with loess(x ~ y) I can see how trends of ...
25 views

### Clustering time-shifted sales time-series

I need to perform clustering and classification of time series of weekly sales of different products. My data are weekly sales of different products in differest areas (stores). The challenges on this ...
26 views

### Feature detection in audio data with feature(s) given as input (preferably using R)

I am curious about options for detecting a given input signal (or collection of similar input signals representing the signal of interest) in audio data, and I am particularly interested in R-based ...
41 views

### How to get the true mean forecast using the Arima package with a Box-Cox transformation

In the Arima package, using a Box-Cox transformation give wrong results when later applied to the forecast method. For example, consider this data: ...
17 views

### R - Network Connectivity over the time

I have to analyze if a network (represented as a graph) mantains its connectivity over the time. The data that I've obtained from the simulation is given here, where L1 points out the vertex of the ...
16 views

### Problem using rsFit to calcualte Hurst Exponent

I am trying to calculate the hurst exponent using the Rescaled range Analysis. I'm using the rsFit function of the fArma package. To test it I create a bronwian motion "r" and the hurst should be .5 ...
45 views

### Is this multivariate normal? 2 time series linked by a common process

Summary: Consider a scenario where you observe the inputs ($X$) to and outputs ($Y$) from a process ($B$). If I have a model describing how $X$ evolves over time, and a similar model for $Y$, how do I ...
96 views

### step by step tutorial for newbie

I'm looking to join the field of statistics and more exactly to forecasting. I'm a software developer and I just started playing with R. Can you recommend me some tutorials related to forecasting, ...
42 views

### What is the frequency of a time series for hourly data?

I am using R for time-series analysis and predictions, the package 'forecast' to be more precise. I am in a dilemma. I have hourly data that needs a prediction and needs to be analysed. I am using the ...
170 views

### Performing a time series ARIMA model on natural gas power demand using the forecast package from R

I've been attempting to forecast natural gas power demand and how it is affected by temperature and price. I'm not sure if I have done everything correctly (relatively new to R), but I do seem to get ...
22 views

### Modelling a time series with the “optimal” combination of N proxy series

I have a time series T. I also a universe U of time series such that A, B, C ... Q are time series that belong to the universe U. My problem decomposes into the following sub tasks: Find a subset ...
48 views

### Which is better, stl or decompose?

I am doing time series analysis using R. I have to decompose my data into trend, seasonal and random component. I have weekly data for 3 years. I have found two functions in R -- ...
57 views

### Fitting a time-varying coefficient DLM

I want to fit a DLM with time-varying coefficients, i.e. an extension to the usual linear regression, $y_t = \theta_1 + \theta_2x_2$. I have a predictor ($x_2$) and a response variable ($y_t$), ...
40 views

### Gather insights from quarterly financial forecast data

I am trying to analyze a quite large (~25,000 rows) dataset of financial forecasts. The forecasts are usually not derived from algorithms, but come from a large number of analysts who forecast the ...
29 views

### Fitting a reduced-form MA(3) time series model in R

I am trying to fit an ARIMA model for a certain financial time series. I've used EViews for modeling, and have decided to fit a so-called reduced-form MA(3) model, where only the third lag is ...
31 views

### Having difficulty forecasting a tslm model

I'm having issues forecasting a model of the following form. y1 <- tslm(data_ts~ season+t+I(t^2)+I(t^3)+0) It fits my data very well, but I run into a problem ...
86 views

### How to determine the correlation between 2 time series while controlling for a 3rd?

I would like to determine the relationship between two variables after controlling for a third. Specifically, I want to know if the prices of mercury and gold over time are correlated with each other ...
63 views

### How to simulate only stationary AR(1) with φ = 0.9?

I am interesting in simulating AR(1) processes with 0.9 parameter and n = 10. The itterations should be 10000. When I was trying to run the program it gave me an error in the estimation procedure. ...
66 views

### Modelling slopes over time

I have data of the price of a product before a newer version came out and after a newer version came out. I'd like to model the slope of the product pre the new product, and post the new product. ...
16 views

### Comparing shape of time series data whist ignoring scale

I have a number of sets of time series data. Specifically each set of time series is associated with a single study subject. Each series measures the area between the vocal chords over a breath period ...
145 views

### What are good ways of plotting distributions over time using R?

I have ~400 individuals and >10k timepoints each (simulation results) I would like to be able to monitor as they change over the course of time. Plotting all individuals is too messy, plotting mean ...
46 views

### Using Moving-average smoothing in forecast package [closed]

I tried to use the non-centred moving average, that means just using past values by setting the option centre = FALSE, but unfortunately you get the centred results. Can anyone detect the failure ...
49 views

### R scripts accompanying the book of “Analysis of Financial Time Series” by Ruey S. Tsay

Are there any R scripts that accompany the book of Analysis of Financial Time Series, Third Edition I went to the book's webpage, and just found the data set.
64 views

### Time-varying Coefficients

I have time series data on fish catches from 1950-2011. I wish to implement a regression model with varying coefficients. I'm aware that cox models etc. exist and implementation via the ...
32 views

### multiple time series classification using randomForest R

I have daily time series of all constituent stocks/members of the S&P500, over a 5 yr horizon, and wish to classify as to whether a stock will report an "earnings revision" via a binary outcome ...
38 views

### The concept of “average run length” in change point detection

With respect to the change point detection for data stream, there is a concept of "average run length", which is discussed in the CPM package manual: I am not clear why that "average number of ...
32 views

### statistical test for sequence of data

There is a sequence of data. It might have different types of trend, i.e., increasing, decreasing, and even cycling pattern. But there is an assumption that these trends should be smooth. (maybe not ...
87 views

### Multivariate biological time series : VAR and seasonality

I have a multivariate time series dataset including interacting biological and environmental variables (plus possibly some exogenous variables). Beside seasonality, there is no clear long-term trend ...
54 views

### How to interpret R stl() output

I want to have a logical interpretation of the results of my stl analysis. ...
24 views

### Time series feature selection: 20 independent variable, 1 dependent variable, 13 observations

I understand the challenges associated with the small # of observations. Nevertheless, some of the predictors have very powerful signals. The question is, how does one use R to most efficiently code ...
61 views

### Seasonality and work days in time series

I have a time series which is daily data for workdays only. There is an almost obvious seasonality comparing the plot of the raw data that appears at the end of each month. However, there are not the ...
76 views

### R - ARIMA model with long seasonal periods - Error: “length of x and xreg does not match”

i want to use an ARIMA model in R for predicting an electrical load on a minutely basis. By examining the ACF I figured out which model could suit. The ACF has shown that the value one day ahead has a ...
89 views

### How to analyse separation of events in time?

I want to test whether the breeding periods of several closely related birds is significantly separated in time. What statistical test should I use? My data consists of several observations per ...
70 views

### ANOVA with time series as dependent variable

I have a randomized experimental dataset with six treatments with each approx. N=60. The outcome is a time-series, namely deforestation in a land-use simulation game over 40 rounds. I managed to show ...
185 views

### How to reduce residual error?

I have a time series as following. It has an upward trend and weak sesonality (found in ACF). I tried spectral analysis, but the residual error kept staying over 600, which is not small enough for a ...
164 views

### Marketing Mix Modeling for sales data

I have data for 4 years with variables year, month and sales volume only. I want to find the base volume and incremental volume. I want to use Marketing Mix Modeling. Can anybody tell how to define ...
58 views

### How do you specify a binary time-series-cross-section model in lme4?

I have a standard binary time-series–cross-section (BTSCS) model that I would like to specify as a mixed effects model using the lme4 package. I've read elsewhere that time-series–cross-section (TSCS) ...
31 views

### Train timeseries model in R and predict purely on out-of-sample data

I have two data files "train.txt' and 'test.txt' with single columns of data. I want to learn a model only on training-data and generate an output on test-data. I can't seem to find a way to do that. ...