Forecasting involves estimating the value or distribution of a random variable which has not yet been observed.

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Forecasting ranges for multiple observations with quarterly data

I have 90 markets with quarterly results, with data from 2014 Q1 to 2016 Q2. I'd like to predict 2016 Q4 results. With a time-series in R, as I understand, you need a single observation over multiple ...
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2answers
18 views

Predictions from betting quotes adjusted for money-flows

Recently at least two predictions were made based on (pools) of bookmaker quotes one for the UEFA Euro 2016 (see here and Zeileis A, Leitner C, Hornik K (2016). “Predictive Bookmaker Consensus Model ...
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12 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 ...
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11 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) ...
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forecast using an ARIMA Model

I'm using the R function auto.arima to fit an arima model for a time series, the result is an ARIMA(2,1,1). After that I apply the ...
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12 views

How to normalised log likelihood, Pitt and Shephard (1999)?

I have a serious problem to understand this paper! Particularly page 13 (or 559)! I have a stocastic volatility model (without factors), namely $$ y_t = \epsilon_t \sigma \exp{\alpha_t/2},\ a_{t+1}=\...
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1answer
20 views

Holt Winters with exogenous regressors in R

I need to forecast using HoltWinters with regression parameters using R. But I found there is not any option of xreg in ...
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9 views

Selection of additive/multiplicative trend/error/seasonality in ETS

Forecasting: principles and practice. This book tells about 30 different types of ETS models. It describes about additive models and multiplicative model. I would like to know on what basis do we ...
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24 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 ...
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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....
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Questions about how to choose the best arima model to forecast

I'm trying to forecast the prices of gold, silver and platinum for the next 10 years using an ARIMa approach. After treating for the basics I am now stuck on the decision of which model to fit best; ...
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9 views

Forecasts from ARIMA(1,1,0) ignore the mean value

I am estimating an ARIMA(1,1,0) + constant model. The program also reports back a mean value. However, the program's forecasts ignore this mean value. Why?
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8 views

How to use Artificial Bee Colony to optimize the gamma and sigma^2 parameter of LSSVM? [on hold]

I have downloaded an Artificial Bee Colony package from http://yarpiz.com/297/ypea114-artificial-bee-colony and tried to apply it into my work on finding ways optimize the gamma and sigma^2 parameter ...
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1answer
60 views

stl() gives seasonal component, but ets() and auto.arima() choose nonseasonal models

I'm completely new to forecasting so please correct me if I'm wrong. I'm trying to forecast sales data using R. My main concern is that when I decompose the data using ...
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1answer
19 views

Diebold - Mariano test for volatility forecasts problem

I am using packages {rugarch} for forecasting and {forecast} for Diebold - Mariano test. As a first step, I am specifying the first AR-GARCH model for financial time series (AAPL Nasdaq) using ...
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11 views

Holt-Winters Damped Method in SAS?

Is there a way to implement the Holt-Winters Damped Method in SAS? In the documentation for PROC ESM it details how to use the Winters Additive and Multiplicative methods but no mention of also ...
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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 ...
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12 views

How do i interpret the output of fourier {forecast}

I have come across a sales forecasting problem. It involves using Fourier transformation. But I have trouble understanding the output. what are the variables and values in the output (fplot1)? how do ...
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1answer
33 views

Is the Brier Score appropriate for ordered categorical data?

According to Wikipedia, the "original definition" of the Brier Score is: $$BS=\frac{1}N\sum_{t=1}^N\sum_{i=1}^R(f_{ti}-o_{ti})^2 $$ Where $R$ is the number of classes, $N$ is the number of ...
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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 ...
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19 views

Correlation for feature selection in multi-dimensional time series

I have multi-dimensional time series data with seven dimensions. The correlation coefficient between two of these dimensions is about 0.65. Can those two variables be said to be/treated like being ...
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1answer
22 views

Modelling Time Series in SPSS

I'm very new to statistics and I've been asked to create a time series model in SPSS. I've taken a list of articles from one journal, each categorized by their main topic (topics have been numerically ...
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25 views

How to manually predict one step ahead time series data using coefficientes estimated by arima function in R

My objective it to manually compute one-step ahead forecast using the estimated coefficientes given by the arima function in R. I will consider the specific model ...
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1answer
20 views

What selection criteria to use and why? (AIC, RMSE, MAPE) - All possible model selection for time series forecasting

I'm performing all possible model selection in SAS for time series forecasting and basically it is fitting like 40 models on the data and shortlisting the 'n' best models based on a selection criteria....
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25 views

Determine best ARIMA model with AICc and RMSE

I have done a training set to fit different ARIMA models and then a test set to assess their performance (with R). From what I understood, I can use the AICc to determine the best model by choosing ...
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19 views

Sales forecasting: Unsure about data grouping

I am trying to implement a simple, short-term (1-4 weeks) forecast of product revenue/sales. The data I have is brand category product revenue where ...
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+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 ...
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19 views

Changing sensitivity (cval) in tsoutliers resulting in unexpected results

I am using the excellent tsoutliers R package to detect outliers (additive outliers, temporary changes etc.), but the cval ...
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32 views

When to use Exponential Smoothing vs ARIMA?

I have recently been refreshing my forecasting knowledge while working on some monthly forecasts at work and reading Rob Hyndman's book but the one place I am struggling is when to use an exponential ...
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25 views

Short-term power load forecasting with a neural network in R

I am working in applying neural network in short term power load forecasting. I have many load input variables and system load data as output variable but, I am confused how to use neural network time ...
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60 views

Forecasting revenue using Monte Carlo Simulation in R

I am trying to forecast revenue for a bank using Monte Carlo Simulation. Revenue is defined as- Revenue=A * B * C * D I am absolutely new to this method. From what I have read, I am planning to ...
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1answer
29 views

ARIMA:-Inf in MAPE and MPE

I am trying to find the forecast accuracy for my ARIMA model. When I use summary(fit) code, I get MPE as -Inf and MAPE as Inf: ...
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1answer
36 views

Intuitive explanation of state space models

Having looked into options for modelling and forecasting a financial time series based on mixed frequency data, I came across state space models, which seems worth exploring. I've however been ...
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31 views

Forecasting with mixed frequency data

Just a general question that I couldn't find too much on. What would be some good approaches to one step ahead forecasting of financial time series with mixed frequencies? Often a lot of the ...
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2answers
27 views

Adding together ARIMA forecasts vs an aggregated model?

Say I am forecasting sales for a company that has four regions using ARIMA models. Each region behaves a little differently so four different ARIMA models are used. In order to forecast overall ...
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1answer
53 views

Sliding window and historical data

In my problem I have a longer period of historical data of a time series. I need to predict for some specific points in time in the future. For these points in time five previous values are also ...
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36 views

Stata rolling window forecast

I aim to forecast the SP500excessreturns using the rolling window option in Stata with the moving window of 120 observations (there are 500 observations in total). The code looks in the following ...
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1answer
46 views

Difference between first one-step ahead forecast and first forecast from fitted model

I'm doing some time series modeling using R and the forecast package, and found a minor difference I couldn't figure out. I'll reproduce my steps below. First, I ...
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1answer
88 views

How to optimise an automatic ARIMA-model selection?

I've been using statsmodels.tsa.arima_model to fit the residual component of some data. I've written an algorithm to automatically select the ARIMA model. Results ...
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2answers
45 views

Sampling from Empirical CDF for Forecasting

I'm trying to forecast the future distribution of a particular interest rate based on its quarterly percentage changes. My assumptions are that: The observations are independent The distribution ...
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Forecasting high frequency exchange rate return

I am working on developing a model to forecast five minute exchange rate return. My return series does not show trend. No auto correlations are significant. How can we develop a model to predict ...
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Theoretical/intuitive question about time-varying Generalized Pareto Distribution

I fitted the GPD to the right tail of nine log return series (I multiplied log returns by -1, so modeling the right tail equals modeling the losses) with a threshold equal to the 95% quantile. Some of ...
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1answer
33 views

Best approach for selecting averaging weights

I am trying to build an R tool for forecasting a (hopefully) wide range of time-series. I have settled on using several models, taking the forecasts from each, and deriving a weighed average of them ...
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1answer
43 views

Which are the basic steps of the stock price forecasting using ARIMA model in R?

I am very new to this field and I want to learn forecasting of stock price using R. Please let me know which are the step should I follow? If someone know tutorial links for forecasting in R then it ...
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22 views

Forecasting a cumulative variable in time series

I'm trying to build a time series model based on a cumulative variable that never decreases. I'm interested in knowing when the observable will reach a certain value (i.e., when it will intersect ...
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27 views

Create weekly forecast of sales data and see impact of weather data on product movement

I am new to R and analytics. I am trying to create weekly forecasting model. Additionally , I have been asked to see if following components impacts product movement : Weather data ( Mean ...
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15 views

Dynamic regression linear models in R

I have a question regarding Dynamic regression linear models. I wonder if it is possible to implement a MLR model (in R) using 'lm' and creating lagged values of predictors and dependent variables. ...
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27 views

What is the difference between GAS ( Generalized Autoregressive Score) model and a GARCH?

I am trying to analyze some data about Brent Oil volatility. So far I have managed to fit a GARCH(1,1) model and an EGARCH. However, someone has recommended to use a GAS model, Generalized ...
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28 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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forecast nonstationary time series and test significance parametrs of AR model

I have a non-stationary time series, wich i want to fit with AR model, first of all i need to take difference wich make my TS stationary, then i see on PACF plot and see that difference number 4 is ...