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

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Determining forecast error of realtime prediction of binary outcomes

Given datasets consisting of a daily prediction and confidence percentage for each of a small number of binary outcomes, what is the proper way to calculate the forecast error of each series and of ...
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21 views

R auto.arima for two variables, forecasting crime deterrence - what am I missing?

I have data on newspaper articles about police cracking down on crime, and data on crimes reported to the police. The data are daily, covering about six months and the same city. I want to try an ...
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15 views

time-series analysis / forecast compared to real planning (controlling) departments?

The following case study: Planning and forecasting in a volatile setting by Amy Wheeler, Nina Weitkamp, Patrick Berlekamp, Johannes Brauer, Andreas Faatz and Hans-Ulrich Holst, published in Rob ...
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7 views

Getting started with age demographic projection

I am trying to project the future age distribution of a population within an organization using (raw) data about the current distribution, as well as past data about entries and exits per age cohort. ...
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2answers
73 views

Time series forecasting with many predictors

Suppose I want to forecast a time series $y$ using its own lags and a large set of potential candidate predictors $X$. The model could be specified as follows: $$y_t = a + \rho \cdot y_{t-1} + \beta ...
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1answer
41 views

Predicting time series using `arima` or `fitlm` in Matlab?

I have 6 sequences (time series); they all belong to the same variable. I divide each sequence in two parts having 80% and leaving the last 20% for validation. I am doing the analysis and modelling in ...
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16 views

Suggestions for Neural Network Structure for Time-Series prediction with constant covariates

I've been working on a time series prediction problem and wondered if someone has run across a similar problem structure & can make a suggestion on how to structure the training data, network, or ...
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18 views

How to interpret the result of tbats{forecast} in R?

If the values of alpha, beta and gamma are not Null it implies that the data has trend and seasonality. What does the negative value of beta or gamma implies? ex - gamma is: -0.0000038297240918093.
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22 views

R : Time Series forecasting with TBATS [closed]

I have a time series dataset with daily values from 2013-10-01 to 2016-01-30. There is a clear monthly seasonality with months October, November recording higher traffic.I tried forecasting using ...
3
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1answer
54 views

Sample size for best forecasting ARIMA model

How can we decide the size or portion of the data given to get the ARIMA that has the best forecasting properties? I mean, for example, we have a hourly series with over 28.000 elements. Which is ...
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29 views

Violating use of information in time-series forecasting

I am trying to forecast stock market returns using a rolling time frame. I want to fit a model on a 20 (trading-) day period and then predict one step ahead - the ...
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22 views

Forecast Daily Cash Flow

I am struggling with a forecast project. I have a time series of daily financial data for a personal account. The goal is to have a time series model for future daily cash flows. Right now,for better ...
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26 views

Time Series regression help

I am having trouble running my multiple regression. I can't seem to prove that the coefficients for the different variables to be statistically significant. My dependent variable is new completions / ...
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1answer
58 views

Irregular Time Series

Please consider the following code (in R) ...
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1answer
43 views

Rolling forecast with DCC-GARCH in R

I have fitted a DCC-GARCH model to my multivariate financial data and do the forecasting. Now, I would like to automate the procedure for a data set that I have. ...
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1answer
39 views

How do I identify slow decay in a seasonal time series?

I have a set of seasonal time series data and I would like to know what method I can use to determine if the data is decaying to 0 or if what I am seeing is actually part of a seasonal drop. By decay ...
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22 views

Comparing variances of forecast errors

I am forecasting a weekly commodity price series. I use a rolling window for estimating my model, and from each window I make point forecasts for one and two steps ahead. I want to investigate ...
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1answer
54 views

Time series analysis for predicting a binary outcome

I'm fairly new to time series analysis. I want to analyze two series of variables in a span of time to predict a binary outcome. For example i collect data over time at my home of two variables: ...
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0answers
10 views

R forecast - How to plot only subset? [migrated]

I'm fitting a model with the R forecast package like this: fit <- auto.arima(df) plot(forecast(fit,h=200)) Which prints the original data frame plus ...
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1answer
42 views

Applying an Arima model with exogenous variables to new data for forecasting [closed]

I have been working with the forecast package in R a lot, recently. And my question might seem trivial (or not, maybe I'm missing something), but for the life of me I can't seem to find a way to fit ...
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28 views

Shewhart and CUSUM control chart for k step ahead forecast error

I am trying to see if the forecast performance has changed over time. I have one to 24 step ahead forecasts. Is it possible to use Shewhart and CUSUM control charts for more than one step ahead ...
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21 views

Gaps of methods to evaluate prediction accuracy

There are many methods to evaluate prediction models based in prediction errors, such as MSE, MAE, MAPE, WMAE, etc. These methods are usually used in data prediction competitions, where one is given a ...
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27 views

Accuracy of time series predicton

I have two time series - actual and predicted. They both can be positive or negative, can jump or remain constant, one can be positive other can be negative - basically any combination is possible ...
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1answer
31 views

Reverse forecasting in time series

we have a given time series includes a specific type of data for example from year 1980 to 2016. Also we know that we should achieve to a predefined goal(a fixed value) in year 2025. But we don't ...
3
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1answer
71 views

How can I determine Gamma distribution parameters from data

I have a time series of weekly retail sales data that I would like to model for an inventory control simulation I am working on. From my research it looks like weekly retail sales like this are best ...
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1answer
89 views

VAR forecasting methodology

I am building a VAR model to forecast the price of an asset and would like to know whether my method is statistically sound, whether the tests I have included are relevant and if more are needed to ...
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1answer
19 views

how to compare ARIMA and exponential smooting model numerically

The exponential smoothing method gives us values like SSE and $R^2$ for the entire model. The ARIMA model, however, does not give us these values. So, given the same data, how do one decide which ...
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15 views

ARIMAX with changing regressor values for t+1, t+2,

I have a question on a quite complicated rolling forecast model Objective I am trying to forecast the number of calls to a hotel. The forecasts I made are t+1 days, t+2 days, up until t+60 days. I ...
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29 views

Error in arima optim

I have the following dataset. I tried arima with xreg ...
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21 views

Multiple Seasons msts R fpp

I am trying to use the fpp package to do some forecasting in R My figures are daily and the seasonal trends include week, month and year. Week and year seem easy: y <- msts(x, ...
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31 views

How to Calculate Standard Error and Prediction Intervals for ARMA Forecasts on Transformed Data?

I have been recently learning about the Box-Jenkins process for ARMA modeling, and I ran into a bit of a wall when it comes to error analysis. In a lot of my data sets, I have to apply a log ...
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1answer
32 views

How can I transfer an ARMAX model in Excel in order to forecast future values?

I am currently trying to set up an Excel based tool, that alows to predict future values based on an ARMAX model, previously set up in SPSS. The Excel tool contains the coeffienients, calculated by ...
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1answer
41 views

Monte Carlo rolling forecast of time series - details needed

I know I'm doing a short term forecast of a volatile time series using Monte Carlo, but I'm unsure as to the details - for example, I'm sure I had a very good reason for naming a term 'drift', but I ...
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37 views

(S)ARIMA — Hints with Time Series

I am a beginner in time series analysis and I would like discuss a couple of numerical examples here implemented in R. I am reading some interesting books, but I also need some expert advice to get ...
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41 views

How to forecast weekly sales data using R and `auto.arima`?

I have weekly sales data with for thousands of products which I want to forecast in an automated manner. What I clearly observe in my data is that there is a weekly skew within a month (wk1 sales < ...
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12 views

Recursive daily forecast [migrated]

I am doing a recursive one-step-ahead daily forecast with different time series models for 2010. For example: ...
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1answer
33 views

Virtually increase the size of sample data

I have a general problem linked to forecasts. I have an operational task to perform and I chose to fit simple models to the data. I would like to have a 6-month forecast month by month for potential ...
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19 views

Reproducing an AR Model

I recently found this paper https://static.googleusercontent.com/media/www.google.com/en//googleblogs/pdfs/google_predicting_the_present.pdf where the authors predict economic trends with Google ...
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1answer
32 views

Forecasting returns similar answer

I am learning forecasting myself and created this sample algorithm ...
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11 views

Correlation but with magnitude included - best fit line?

I want to analyze which industry forecast best predicts my business. I have 2 to choose from. I have found that they are all highly correlated, but I suspect one may be also closely related by the ...
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1answer
7 views

Calculating Reorder Quantity

I am using the Holts Winter Forecasting Model to predict 1 period, and all the results from testing various items have U-Stat Scores below 1. I already know average and variability for supplier lead ...
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2answers
112 views

Time series with autoregressive distributed lags: Forecasting for future

I have daily data from last 2 years. I want to do ARIMAX and the regressor component being autoregressive distributed lag of the same variable. Since it has impact, along with dummy variables to ...
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15 views

R - Binary outcome from binary history [closed]

I am new user of R and I am trying to solve an issue: I have a series of 1,0 (which represents data taken in a period) and I would like to predict future with same results (1,0). Better explanation: ...
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19 views

Variance on Extreme Seasonal Time Series

I'm trying to come up with a decent method for forecasting a unique seasonal time series that is involving multiple periods of seasonality: Weekly, Monthly, Quarterly and I am stuck because I have ...
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1answer
57 views

Forecasting and making sense of data

This might seem very elementary or even silly, but all comments are much appreciated. As science major I do not have deep technical knowledge in statistics, so any guidance is much appreciated. Say ...
3
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1answer
45 views

Time series: probability to exceed a certain threshold

I'm a beginner and I have a generic time series and I want to know the probability that it reach a certain threshold in the future. For example, the time series is day average temperature and I want ...
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41 views

Compare forecast models accuracy

I have a time series of number of visitors of an website for two years. I have to do the forecast of the number of visitors for the next semestre. To do so, I used three forecast models: ARIMA, ...
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62 views

Daily data forecast: How to specify day of week and month of the year seasonality in SAS

I have daily data for 2 years starting from jan1 2014 till december 31 2015. I want to forecast for next 365 days using this data set. I am using below code. ...
3
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2answers
149 views

Length of Time-Series for Forecasting Modeling

I'm working with mixed model for forecasting analysis. One of the decision that we want to take for the modeling is length of time-series, whether it should be 2 years or three years. So my question ...