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

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Normalizing year data

I have a data set in which there are data from April 2010 to Dec 2010 ( 9 Months) Jan 2011 to Dec 2011 (12 Months ) Jan 2012 to Dec 2012 (12 Months) Jan 2013 to Dec 2013 (12 Months) Jan 2014 to ...
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10 views

Multivariate analysis of various tIme-series data

I have to analyze a time series data, which has multiple explanatory variables (x) and the data also has multiple dependent variables (y). The structure of data is as under: y= Sales of Brand(1), ...
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Contradiction in the ADF (Augmented Dickey-Fuller) and KPSS (Kwiatkowski–Phillips–Schmidt–Shin) tests for financial time series

I use the ADF and KPSS to test for stationarity / non-stationarity of price increments in financial time series. The two test applied provide different results for low lags, but the same result for ...
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1answer
10 views

Predicting time series value given a threshold weight

I have 2 datasets. One is time series data of sale of homes by region by type: ...
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8 views

Estimating mean and autocovariance function: stationary case

I have found this question in a past exam of my course in Time Series Analysis: De fine the mean function and the autocovariance function for a univariate time series $(Y_t; t \geq1)$, in the ...
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26 views

Identify outlier usage intervals in time-series data

I want to find outliers in power consumption in real-time, at hourly rate, i.e., at the end of the hour, I should say whether power consumption in current hour was outlier/anomalous or not. Approach: ...
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12 views

Roadmap to forecasting needed! [on hold]

I'm working in R and have been trying to forecast GDP by using the spread between Treasuries and corporate bonds. I've ran into some trouble though as I don't have a clear understanding of the steps ...
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16 views

VAR lag length vs Johansen cointegration test outcome?

First puzzle: I am taught that the lag order of VECM does not affect the cointegration rank because the lag order is for the differenced regressors. But, I see the contrary: I experimented with sample ...
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18 views

Hidden Markov Model and State Space models

I am pretty sure that Hidden Markov models are a particular instance of state-space model but I cannot understand in what they are "particular". I mean, state-space models are based on two ...
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Prewhitening Regressors in Lagged Time Series Regression

I'm trying to identify significant lags in a time series regression such that $Y = \beta_0X_t + \beta_1X_{t-1} + ... + \beta_iX_{t-i} + \alpha_0Z_t + \alpha_1Z_{t-1} + ... + \alpha_jZ_{t-j}$ I ...
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Maximum Likelihood estimation and the Kalman filter

I know the Kalman filter recursions and can derive these but what I don't really get is how to estimate the hyper parameters using maximum likelihood. I understand that when running the Kalman filter ...
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13 views

Testing a non-AR time series for mean reversion

If one discretizes Heston's dynamics for the instantaneous variance of the stock price, one gets the following time series : $$ V_k - V_{k-1} = a + b V_{k-1} + c \sqrt{V_{k-1}} \mathcal{N} \left( 0, ...
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fitting a dynamic bayesian model to irregular time data

I have a dynamic epidemiological model which I solve with scipy's ODEint and fit to my data using pymc. My data is irregular in ...
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25 views

How many observations to estimate a parameter of an Archimedean copula?

Let's consider for example the bivariate Gumbel copula. $$C(u_1, u_2)=exp \left[-\left((-ln(u_1))^{\theta}+(-ln(u_2))^{\theta}\right)^{\frac{1}{\theta}}\right]$$ In R there are some functions (such as ...
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7 views

Linear regression (best fit line) on moving averages vs raw data?

I have a series of sets of data over a period of time, with the amount of data available being quite variable between sets. One has points for almost every day but is really quite noisy; another has ...
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9 views

Hourly electricity demand data finding AR and MA terms

I am new to time series analysis. I have hourly electricity demand data for five years (having multiple seasonalities as daily,weekly,annually) and I want to guess the number of AR and MA terms using ...
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7 views

Measuring the effect of weather on retail sales

I'm currently working on modeling this as an ad hoc. Sr mgmt want to know how much of our sales growth during the year can be attributed to weather. I chose to investigate "weather" as temp & ...
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Can I have a time covariate to account for temporal trends as well as time series biases such as AR/MA?

Can I have a time covariate to account for temporal trends as well as time series biases such as AR/MA? For instance if I have a model: Y ~ X + t where t is time, then can this be an alternative ...
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1answer
18 views

Time as random effect or fixed effect in glmmADMB

I have a longitudinal dataset where patients have a measurement with a date, currently coded as time from end of treatment (days). Now, I want to build a model. Roughly, a zero inflated Poisson model ...
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Should we predict residual of ensemble model and add it to final prediction?

We have been doing a time series project on daily wise data. We built 5 different models SVM, XGBoot, ARIMA, KNN, ANN. We then built predictive models for residuals for all of these models and got ...
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1answer
20 views

Time Series Forecasting, Log or non-Log

I have read that you should use log transformations when the fluctuations on your data are increasing over time, but what do you do if the fluctuations level out over time? A plot of the time ...
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1answer
14 views

Time Series Modelling[Issue with modelling the residuals]

I am doing the sales forecast. I found the trend and seasonality manually for my time series data. Regressed time series data against the trend and seasonality and found the residuals. The residuals ...
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1answer
35 views

Interpreting Time series regression/bivariate sorts

I am somewhat unsure how to interpret some result from an analysis that I have done on two independent variables and a dependent variable. My goal is to test whether the abnormal return difference on ...
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8 views

Imputation for Time Series of Accumulated Value

I have a regular time series of accumulated values of a variable (usage) with some missing (sometimes consecutive) intervals. Is there an imputation method that methodologically considers this ...
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28 views

Analysis of Time Series Data in R [closed]

I am attempting to use the 'forecast' package. I have a series of 54 monthly observations, read from a CSV file, and converted to a time series. Here is the raw data: ...
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DCC models in R: how is the first starting value chosen?

The DCC model is defined through the proxy $Q_t$ as $$Q_t=(1-\alpha-\beta) \overline{Q} +\alpha\epsilon_{t-1}\epsilon_{t-1}' + \beta Q_{t-1}$$which is then normalized to find the correlation matrix ...
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Fractional Gaussian noise, the KPSS test, and stationarity

Fractional Gaussian noise (fGn) is characterized by the mean ($\mu$), the standard deviation ($\sigma$), and the Hurst index ($H$). It's my understanding that it is stationary, for the simple reason ...
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19 views

Predicting the status of an individual across time [closed]

In my case my training data frame is like the following : Id|Month|Status|Others 1|01/16|O|X 2|01/16|O|X 3|01/16|O|X 1|02/16|E|X 2|02/16|O|X 3|02/16|F|X 1|03/16|E|X 2|03/16|O|X 3|03/16|F|X ...
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1answer
34 views

How to determine seasonality of a binary variable?

I have a dependent binary variable Y, and an independent date variable X. I want to find out if there is any seasonality (at the year level). A few notes: The binary variable is in my model ...
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1answer
26 views

How does trend stationary recovers from shocks in long run?

I was trying to understand difference between drift and trend wherein I came across concepts of unit roots and trend stationary. (I haven't read any books on time series, just going through web). ...
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20 views

Is HoltWinters() inferior to ets()? [closed]

here is a simple R program using HoltWinters() function.it gives the following error message even for 100 iterations..(but run smoothly for smaller number of iterations)..But ets() handles the ...
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Threshold cointegration

I have a panel data N=45 T=25. Engle-Granger test confirms co-integration between two I(1) variables. I would like to test for threshold cointegration between these two vars. Is there a user written ...
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Decomposition of SARIMA models

I use R for time series analysis. I would like to evaluate decomposition algorithms. decompose and stl from "stats" package lead ...
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98 views

Why is my kalman filter trusting so much my observations?

This question follows the one asked there. I am trying to filter an equity index (Stoxx 600) time series using kalman filter. I'm using the R package dlm and my code is inspired from the dlm ...
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22 views

Best step pattern for unconstrained/subsequence DTW(Dynamic Time Warping)

I am implementing an unconstrained/subsequence DTW algorithm (in R). The query of data which I am trying to find a match within the reference data is much smaller (as compared to reference). I have ...
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Critical point in time series and pattern comparison

I have some data from a recent experiment. Two factors, each had 2 levels, resulting in 4 conditions. In each condition there were 12 participants, so 48 participants in total. Each participant ...
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29 views

Time series analysis of electricity load questions

I have hourly data of electricity load (MW) that span 8 months (that is, 5760 data points). I also have predictions from a regression model for the same period. My goal is: to examine some ...
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1answer
22 views

When to run truncated backpropagation through time in recurrent neural networks?

I'm interested in training recurrent neural networks using truncated packpropagation through time (BPTT). From Sutskever (2013): Truncated BPTT...processes the sequence one timestep at a time, ...
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What is the difference between the Monte Carlo Method in R package 'DMwR' and a normal Monte Carlo Method?

I am trying to estimate the performance of a machine learning model on time series data. I saw the example of model evaluation using Monte Carlo Estimates from the book "Data Mining With R Learning ...
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21 views

Generalized Hurst Exponent - What value to specify for $\tau_{\max}$?

Consider a time series $X: S \to \mathbb{R}$, where $S := \{\nu, 2\nu, 3\nu, \ldots T\}$, and $T$ is a multiple of $\nu > 0$. For each $\tau \in (0, \tau_{\max}] \cap S$ and $q \in \mathbb{N}$, ...
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Scaling predictors in ARIMA model

If a predictor in an ARIMA model has much lower magnitude than the variable you are trying to predict, then do you need to multiply it by a scalar in order for it to be an effective predictor in the ...
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22 views

Why do VAR forecasted values radically change depending on which month historical data end?

I am building a model to forecast housing variables using vector autoregression. I am encountering spurious results. My forecasted values change dramatically depending in which month the historical ...
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Negatively Correlated Predictors in Arima Model

If a predictor is negatively correlated with a variable you are trying to forecast in an Arima model, will Arima pick up the negative correlation when you add the predictor in the xreg argument? Is ...
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5 views

Combining Lower Correlation Predictors to Create Higher Correlation Predictors

I'm working on an Arima model to forecast a given variable and so I'm looking in my data for variables with correlation to the variable I'm trying to predict, to add as predictors in the xreg ...
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1answer
21 views

Does picking randomly from an imbalanced group balance the group?

If I had a bag of marbles with 75% blue and 25% red (ratio is what matters not raw number, so this applies to 100 marbles, 1000 marbles, 100000 marbles) So if I had this imbalance of 75% blue and 25% ...
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How do I interpret ACF and PACF figures from a SARIMA model? [closed]

I have data about tourism arrival. the plot of data is this is result after first differencing So, what is SARIMA model for this problem?
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Difference between SUTSE (Seemingly Unrelated Time Series Equations) and SUR (Seemingly Unrelated Regressions)

I am studying time-series econometrics and in particular Dynamic Linear Models for multivariate time-series. Someone can help me in understanding which is the difference between SUTSE (Seemingly ...
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Lag-free filter methods for time series

I'm currently working with accelerometer based raw data (100 hz). Now I want to low pass filter this timeseries of accelerations for further analyses. I tried different filters like the simple moving ...
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What to do about “sounds like spam” error? [migrated]

I attempted to post a detailed question, focused on conceptual and statistical issues, not coding. The site rejected my post with an unexplained error message that my post looked like spam. I also ...
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General-to-specific subset selection (“Autometrics”) performing well in macroeconomics

I wonder why general-to-specific (GETS) subset selection and particularly the Autometrics algorithm are performing well in macroeconomic modelling/forecasting. How does Autometrics work? Doornik ...