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

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Incorporating intraday data into end-of-day forecast

my target variable is observable intraday but I am interested only in EOD forecasts. I will denote the variable $\ y_{D,24}$ as the reading of interest for day D is ...
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4 views

Interpretation of Lo.Mac M1 and M2 test statistics

I used R to get the Lo.Mac test result for a return series. I am not sure how to interpret the M1 and M2 test statistics of Lo.Mac variance ratio test. What are the critical values to reject or accept ...
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65 views

Detecting changes in time series (R example)

I would like to detect changes in time series data, which usually has the same shape. So far I've worked with the changepoint package for R and the ...
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4 views

How to find anti-correlated subsequences in correlated time series?

Say I have two time series $X_t$ and $Y_t$ (with $ 1 \leq t \leq N$), which have a high positive Pearson correlation. Say I also have reason to believe there are subsequences $X_{tj}, Y_{tj}$ (where, ...
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28 views

Let's talk sales forecasts - integrating a time series model with subjective “predictions/ leads” from sales team

I've learned a lot about time series forecasting this previous year, but one thing that's still a bit lacking in terms of a formal system is integrating a future sales projection into an existing time ...
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21 views

Which program for PVAR? [on hold]

I have been looking at some studies that use panel vector autoregression models. The problem is, I can't find which statistical package the authors use. I would like to estimate impulse responses in ...
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13 views

Technical Indicators reference [migrated]

I have been looking for a good reference where I can find how technical indicators of stock market analysis are calculated. I have a dataset (time series) which I want to extract these indicators to ...
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2answers
37 views

Detect periodic events within data

I have a collection of card transactions, each with a date, amount, card identifier and merchant. I want to determine if a card is making periodic payments to a given merchant. The issue is that the ...
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17 views

Multivariate stochastic time series forecasting in R

Hi I have a multivariate time series like this ...
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11 views

Slope versus Slope Change [on hold]

Is there a difference in the definition of Slope versus Slope Change? I am doing slope and level change calculations for my dissertation. I can't seem to find the answer if there is a difference.
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1answer
45 views

Detecting anomalies in a time series where new data points will be continuously added

I have a time series data and I will be adding more data points in a consistent manner. I want to figure out whether the new data point added is an outlier, in regards to the previously observed data ...
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19 views

Decomposing a known time series into a linear combination of known timeseries.

I'm have a time series that is dependent on a large number of other timeseries, but these dependent timeseries don't add up to the main one, as I don't have the full population of these dependent ...
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5 views

What Model to Use, are my assumptions correct? Psuedo Time series

I have quite a few questions and would like some help/advice or a general pointing i the correct direction. I have a dataset that has every home sold over the last 3 years and it's sale price, along ...
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29 views

Matlab: Problem in implementaion using toolbox while learning Kalman FIlter

I have AR(1) model with data samples $N=500$ that is driven by a random input sequence $x$. THe observation $y$ is corrupted with measurement noise $v$ of zero mean. The model is $y(t) = ay(t-1) + ...
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1answer
30 views

Regressing a discrete variable

I have a discrete dependent variable (say, number of units bought) and want to run a linear regression with in-store promotion, seasonality, trend etc. as predictor variables. I'm not sure if it is ...
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1answer
31 views

Normalize time series with different lengths with linear interpolation in R

I have a large set of time series (100k, each 3 observations), their lengths varies about 10% on average. Each of them cover the time interval of the same lengths but varies due to rate of sampling, ...
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31 views

AIC versus cross validation in time series

I am interested in model selection in a time series setting. For concreteness, suppose I want to select an ARMA model from a pool of ARMA models with different lag orders. The ultimate intent is ...
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13 views

Prediction intervals for mixture models for time series forecasting - is it really an average of the prediction intervals of the averaged models?

I'm trying to find out how to do forecasting with a mixture model (averaging the forecasts of an ets, an arima and an ...
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16 views

how i can model VAR-GARCH

i really need your help how i can run the ling and McAleer(2003) model (VAR-GARCH) and McAleer (2009) model(VAR-AGARCH) with spillover response? and can you help me how i can run DCC-EGARCH with ...
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10 views

time series with error bounds at lower level than “series”

I have what I think is a very basic question but I am greatly struggling with knowing what model to use and where to start, and all help would be appreciated. Basically I have a dataset that is a ...
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11 views

What interpolation methods to use for irregularly sampled time series?

I have two AR(1) time series with a pre-defined cross correlation from which I sample using a Gamma distribution to obtain irregular time series. What interpolation methods can I use to obtain a ...
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1answer
25 views

Unit Root testing and stationarity of a time series

I'm trying to understand: how is check for stationarity(or lack thereoff) linked to unit root testing. More so the logic of it. i understand the null hypothesis used in adf or kpss but I need the ...
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1answer
28 views

Where do you find info about which predictive distribution an algorithm uses for forecasting?

I am trying to fit a mixture model to a time series in order to make forecasts. I'm told that this is quite straightforward as long as the predictive distributions used by the component algorithms ...
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19 views

Level of a time series and adding daily dates to plot

just wondering if you can help me with explaining this plot Just wondering what does level tell me? is it the trend of the data with seasonality taken out, which is the slope right? Can't seem to ...
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1answer
33 views

Regression with different frequency

I am trying to run a simple regression but my Y variables is observed on a monthly frequency and x variables are observed on an annual frequency. I will really appreciate some guidance on a suitable ...
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1answer
16 views

What statistic to use to measure effectiveness of treatment on fluctuating process

I have a process $R$ that normally does something like a random walk between 0 and 1. I have a set of treatments. I believe that some of the treatments will bias the process $R$ in such a way that, ...
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64 views

ARMA model in R

I am a bit confused using the arma() function in R regarding interpretation. So what exactly is the equation of a for example AR(1,0,2) given the output AR1, MA1, ...
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13 views

Help forming a VAR model

Can anyone help me for a very basic VAR model for regressing Inflation (CPI first difference) on energy prices and money supply. any suggestions be appreciated.
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22 views

How to use lagged dependent variables (panel data) in practice?

Working with a panel data set with a daily time series structure I was told to include a lagged dependent variable. The dependent variable is daily electricity consumption of a medium size sample ...
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1answer
152 views

Interpretation of (scale of) AIC, AICc and BIC when comparing different models

I'm trying to fit a model to a time series, but I am pretty confused as to which is the best. I'm looking at an arima model, and ets model and an stlf model, which each performed best within their ...
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27 views

how to predict the time when event occurs

I have time series data. The data looks like the following: ...
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18 views

Passing different forecasting method to hierarchical time series forecast in R? [migrated]

I have a hierarchical time series, the bottom level series of which all exhibit intermittent demand. It seems advantageous to use Hyndman's HTS package for optimal combination within the hierarchy. It ...
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1answer
27 views

Stationarity of AR(1) process whose autoregressive parameter could change over time

Imagine an AR(1) has an autoregressive parameter which could change in time. $y_t-\mu=\phi_t (y_{t-1}-\mu)+\varepsilon_t\,$, where $\phi_t$ is not always constant but still lies inside the usual ...
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How to add NA's to the data not available for some dates? [migrated]

I have a data for short term electricity load forecasting. I have to clean the data, adding NA's in the data for dates( and blocks) with no data. For example: 1st case: with some dates missing: ...
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26 views

Seasonal Indexes adding to zero

In the textbook Forecasting: principles and practice by Hyndman and Athana­sopou­los, in the Classical Decomposition (Sec 6.3), in step 3 of the additive decomposition algorithm, the authors state ...
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1answer
52 views

ARIMA Specification from Correlogram

How should I determine the data generating process from the correlogram below? This is non-seasonally adjusted monthly data that has been 1st differenced. I am trying to conduct univariate time ...
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3answers
171 views

How can you tell if a yearly increase in population is statistically significant?

I have daily data for two years consisting of "number of sightings" each day. Is there a way for me to test whether the data for year 2 is "significantly" higher than the first year? I know the ...
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2answers
40 views

Symmetry in moving average smoothing in “Forecasting: principles and practice”

In the textbook Forecasting: principles and practice by Hyndman and Athana­sopou­los, in the moving average smoothing section (Sec 6.2), the authors speak of even order moving average smoothing not ...
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1answer
93 views
+50

Measuring length of intervention effect

I ran a study in which participants were randomized to either a control or an intervention, with outcomes in the form of time-to-event data. While overall time-to-event is shorter in the intervention ...
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18 views

Autoregressive Model

I am currently attempting to build a regression model explaining Current Inflation as measure by monthly CPI. I am considering the following model; CPI = B0 + B1(LAG_CPI) + B2(Lag_Oil_Price) + ...
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13 views

Vector Autoregression, how to interpret Impulse Response Function (IRF)

I have an IRF that shows the GDP shock to GDP. Let's say I have a 5-year forecast of GDP. If there is an immediate 1% decrease in GDP today, can I adjust the original 5-year forecast by using the ...
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49 views

Interpretation of the autocorrelation plot

This plot indicates the autocorrelation for a monthly time series of household gas consumption. This plot clearly shows a seasonality, I was wondering if the repetitive positive and negative ...
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1answer
40 views

Issues in auto.arima algorithm when using external regressors and outlier correction

auto.arima is an automatic arima modeling function in forecast package in R that uses information criterion(example: AIC/BIC) to ...
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8 views

tbats{forecast} in R gives strange predictions for some folds in cross validation

My daily data shows weekly and yearly seasonality, so I decide to try the tbats function. When I first fit the model with all the data, it worked fine. However ...
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1answer
13 views

Choosing the right model for prediction

Given a set of temperatures of different cities for a month, which prediction model should I use for a two day look ahead prediction? Regression models or Time series?
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222 views

A 'Pure' Time Series Model

Can anyone explain to me what is meant by a 'pure' time series model? I believe it might have something to do with the exclusion of external factors but I'm really not too sure. For example, the ...
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29 views

Analyzing Independently Pooled Cross Sectional data (TIme series)

I am new with time-series data analysis. It seems that there is some confusion in terminology (Panel, and time-series cross-sectional). But my question is general in nature. How do I analyze a ...
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13 views

Time series causation and probability

I have a series for an individual that looks like this: There are observations of the individual at random times. At each time they may experience an event and the outcome fo that event is binary ...
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1answer
19 views

Using regression tree on time series data

I have been looking around for resources on applying a regression tree in an attempt to understanding how various spend variables impact a companies revenue overtime. Is this type of analysis ...