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

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Difference between Time delayed neural networks and Recurrent neural networks

I would like to use a Neural Network to predict financial time series. I come from an IT background and have some knowledge of Neural Networks and I have been reading about these: TDNN RNN I have ...
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5 views

Hierarchy predictive top down approach

I'm having a problem with using a hierarchical top down forecasting approach. According to my understanding, when I split an aggregated value on the levels below it, I have to know the percentages ...
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1answer
14 views

Cross-validating the tbats/bats function in forecast

Is there a way to cross validate the tbats/bats function in the forecast package in R? I have been trying to get CV weighted parameters which then I can pass to a function for revised estimates. ...
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34 views

Forecasting: Turn a basic formula to an ARIMA model

What ARIMA model best represents a formula like this one. $$R_{T_i}=\frac{R_{T_{i-12}}+R_{T_{i-24}}}{2}\times{TREND}$$ I thought that an ...
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1answer
20 views

Forecasting with no seasonality

I have a set of data, let's say average weight of employees, captured every month over a period of 5 years (2010 - 2014). I cannot find a seasonality trend in the data over these years. Also, I have ...
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31 views

R: Time series forecasting alternatives to ARIMA [closed]

I was using WEKA to perform time series forecasting based on lagged variables and machine learning algorithms: Time Series Analysis and Forecasting with Weka I am trying now to do something similar ...
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14 views

Forecasting with Dynamic values using R [migrated]

I have a Json object : {"tcDetails":[{"project_nm":"abc","id":"1","n_tc":"32","TC": [{"29/06/2015":50,"30/06/2015":45,.....}] {"level":[{80,85,90,95}]}]} ...
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15 views

Eviews and Forecasting Linear Regression with AR(1) Error Term

This question is geared towards those who are familiar with Eviews and forecasting with linear regression in the case of AR(1) error terms. Consider the classical linear regression model where the ...
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14 views

Not able to make daily time series analysis in R [closed]

The following is my data, ...
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42 views

Is there a name for this fallacy?

I sometimes encounter a view that only perfect forecasting is really forecasting. For example, if I claim that I have a model which forecasts election results, people will think I'm making the ...
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33 views

Choosing the right forecasting method [closed]

I have 27 years monthly price data of a commodity. I want to forecast the price for the next six months. How do I choose the right forecasting technique? The data has both trend and seasonality. I ...
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22 views

why nsdiffs() returns this time series is not seasonal while it looks seasonal?

nsdiffs() returns this data is not seasonal and hence no seasonal differencing is required. nsdiffs(TrainTs, m=frequency(TrainTs), test=c("ocsb","ch"), max.D=8) Error in nsdiffs(TrainTs, m ...
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13 views

Forecasting values along with corresponding years [migrated]

I have a sample data set (named as s3) in the following manner: ...
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33 views

Seasonality in residuals [closed]

I'm running a simple OLS with two seasonally adjusted independent variables and the dependent variable is also seasonally adjusted. I'm seeing distinct seasonality in the residuals of the estimation. ...
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1answer
52 views

Correct procedures to detect and correct outliers for aggregated/SKU time series

Background I am currently working with sets of product sales time series at SKU-level for a FMCG company. Data are available in a weekly format for multiple years and sales data for hundreds of ...
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1answer
37 views

How to compare ARIMA model in R to actual observations used to create the model?

I've been using the R forecast package's auto.arima() function to fit an ARIMA model to my time series data. I want to see how good of a fit the ARIMA model is to my original data. I hope to plot my ...
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20 views

Selecting optimal sample rate for time series prediction

Is there a procedure to choose the optimal sample rate (every second, minute, hour, ...) for time series prediction (say fitting an ARMA model)? I guess it depends on how many steps I want to predict ...
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2answers
54 views

Seasonal arima forecast equation

I need to compute a seasonal arima model, and make forecasts about vehicular traffic. My idea is to compute the model with R, and use the AR and MA coefficients in another application to predict ...
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1answer
35 views

How can a 95% confidence interval not overlap with my trendline forecast?

I used holt winters in excel to forecast 12 moths ahead based on 40 months of historic data. Then I ran a monte carlo simulation to create 1000 scenarios and computed upper and lower bounds to create ...
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3answers
139 views

How do I predict a time series with the help of other forecast time series?

If I have $n$ measured and interdependent time series $M_1, M_2, M_3..., M_n$ and have $n-1$ forecast time series $P_1, P_2, P_3..., P_{n-1}$, how can I predict the last forecast time series $P_n$? ...
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24 views

Proc UCM Forecast Series

I'm forecasting a data series with one time dependent variable (GDP) and one 0 1 time indicator "Flag" (0 starting at February 2014, 1 before that). When I use proc ucm with the below options, it ...
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1answer
49 views

Predicting water levels based on rainfall stats

I am curious if R or any other open source code can deal with forecasting changes in water elevation based on a predicted/forecasted value of rain. I have a ton of data that shows water elevations ...
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9 views

What are the criteria to choose for a bootstrapping, frequency, forecasting of fitting method to fit demand data?

My goal is to calculate the inventory height of several products. To do so, I have to calculate the probabillity a certain demand occurres. However to determine the distribution based on historical ...
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55 views

Attrition Forecasting

I am currently trying to develop a forecast for monthly subscriber attrition that allows me to predict for a future point in time, how many subscribers I have. I have a couple of years worth of ...
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10 views

Assessing prediction accuracy for outcomes with varied event dates

We are interested in coming up with a way to measure accuracy of prediction in forecasting durations to an expected event. On date X, individuals are asked to estimate when a certain event will occur ...
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1answer
21 views

Rolling samples, choosing a number of observations

I want to estimate a forecasting equation for monthly data. I'm essentially trying to find out how to balance the stability of using a longer time series to estimate the equation versus the fact that ...
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1answer
35 views

Combining Forecasts

I am in the process of creating one well-rounded forecast, and in my research I found a few mentionings approving the use of several forecasts combined into one. I really like this idea but I have ...
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22 views

What's the nowcasting “bible”?

Is there an accepted best text about nowcasting that you would recommend for someone getting into the field?
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26 views

How many observations are needed to make an RMSE meaningful

I have a relatively short monthly time series (7 years). I'm wondering if I estimate an OLS model with 6 years of data and do pseudo-out of sample forecasting with the remaining year, would the RMSE ...
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1answer
41 views

Using ARMA model for future forecasting

I just started learning about times-series modeling and I'm confused by the following scenario: Let's assume we train a ARMA(p, q) model on a time-series $\{x_1, x_2, ..., x_t\}$. Later in a test ...
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12 views

Calculating MASE Scores for Individual Participants

I have run a controlled experiment in which 30 participants made one step ahead forecasts under two separate conditions. I want to know if there is a difference in error scores between the two ...
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1answer
40 views

time series forecasting using auto.arima and exponential smoothing

I am working with workers’ remittance quarterly data for Bangladesh. Here I am doing time series forecasting using R. I am applying auto.arima model and exponential smoothing model. I want to compare ...
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39 views

Empirical Prediction interval for time series forecast based on quantile regression

As Gardner notes "almost all point forecasts are wrong", so prediction intervals (PI) are necessary to quantify uncertainty and help us make informed decisions. There exists theoretical PI, and in ...
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22 views

How to treat non stationary independent variables when our dependent is stationary under co-integration?

I am conducting Grangers causality test. I have 14 variables. My dependent (y) and 12 independent variables are found to be stationary at first difference. But the remaining 2 dependent variables are ...
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1answer
38 views

Holt-Winters Forecasting - Why do we use most recent estimate for all projections going forward?

Ive been doing some research on using the Holt-Winters method for forecasting and understand all but one aspect. Why do we use the most recent estimate for the base and trend components for all ...
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22 views

Getting exactly same forecasted values in auto arima [duplicate]

I am using auto.arima from forecast package for time series. The auto.arima selects best ...
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18 views

Evaluating parameters of a time series model on multiple experimental sessions

I'm trying to evaluate a model for a time series, given many time series (plural). For example, i'm using the forecast package and in particular the ...
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2answers
40 views

Why don't we write the error term with forecasts?

A minor issue that's been annyong me as I blindly write it... As an example for a simple AR(1) process $$y_{t} = Ay_{t-1} + \varepsilon_{t}$$ I can write the process at time t+1: $$y_{T+1} = Ay_{T} + ...
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72 views

Variance of the future value of a cash flow

I want to compute the variance of $FV =A\sum_{k=1}^n(1+r)^k=:g(r)$, assuming interest rate $r\sim\mathrm{N}(\mu,\sigma^2)$ and constant equal payments $A$. As discussed in the comments, the Delta ...
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21 views

Making forecasts in minitab only positive

I'm looking at 30 years of rainfall data, working with the daily average rainfall per month (That sounds weird... it's the total rainfall in the month, divided by number of days in the month). ...
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2answers
28 views

How can I forecast interrelated hierarchies?

I need to model demand for server components. Server 1 & Server 2 both use Hard Drive B, Server 1 uses Network Card A, and Server 2 uses Network Card C. ...
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24 views

How to model “aggregate” dependent variable in case of variable transformation?

Background: I have a panel data set consisting of dependent variable $y_{i,t}$ and several independent variables $x_{j}$, where $i$ indicates observation (ID), $j$ serves as dependent variable index ...
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1answer
32 views

Forecasting a time series with conditional variance (heteroscedasticity) using Arima

I want to forecast a time series and have reason to believe that there are heteroscedastic errors/variance, which could be modelled with GARCH. However, I am not really interested in ...
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25 views

How does R calculate prediction intervals in the forecast package?

I have a large dataset with different factors that I want to forecast to the future. These forecasts I will then later on use as inputs for a Monte Carlo simulation. My idea would be to use arima ...
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16 views

Exponential Smoothing with Causal Regressors

I am trying to develop several approaches to analyze the effect of covariates on retail sales . the first approach i am trying to use is exponential smoothing with regressors (for its simplicity to ...
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2answers
145 views

Forecasting: Is correct to say “If the time series is non-stationary” don't use ARIMA models?

In case I dont want to "pre-process" the time series. I do a unit root test, and if it gives that is a non-stationary time series, then I will stay away from ARIMA models. Is this correct?
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1answer
85 views

Linear regression with ARIMA errors variables selection

I have constructed linear regression model with ARIMA errors. Here is an output: Standard error of my IV coefficient seems to be very large compared to the coefficient itself. Can I conclude that ...
2
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1answer
41 views

GARCH modelling and forecasting

I have a few questions regarding GARCH modelling and forecasting and it would be great if someone could help me. I am modelling oil spot prices log-return using various GARCH models: GARCH, APARCH, ...
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2answers
81 views

GARCH forecasting in R: constant mean forecast!

I'm trying to forecast a time series of a stock option using ARMA-GARCH modelling in R. First I determine the ARMA order using AIC and I found (0,1) to be the best one. But when I run ...