Questions tagged [forecasting]

Prediction of the future events. It is a special case of [prediction], in the context of [time-series].

1,146 questions with no upvoted or accepted answers
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259 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
138 views

Sequential semi-automatic model selection of time series forecasting

I have a number of univariate time series that I would like to incorporate in a production system. I have daily data from a month and I would like to forecast every day the corresponding values for ...
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47 views

Forecasting an individual based on a representative group

I’m trying to forecast demand for an individual based on historical data of many individuals, but I’m having trouble finding examples of this. For example: I want to forecast the demand for a single ...
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1k views

Issue when building VAR model using Python

Building on my Question here which involved predicting the closing price of a stock given the previous days closing price, opening price, high price, low price and the number of articles associated ...
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901 views

tbats with weekly, monthly and yearly seasonality not working

I am trying to predict values based on a dataset which may contain weekly, monthly and yearly seasonal data. To simplify things I am assuming that all months have four weeks (28 days) and the year has ...
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526 views

Does “level” in exponential smoothing stand for the “mean”?

In triple exponential smoothing it is said that there are estimates for 3 components: level, trend and seasonal. Does "level" here stand for "mean"? In single exponential smoothing is only the level ...
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286 views

How to create sklearn random forest model identical to R randomForest?

In R I usually define Random Forest as follows (an example): ...
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155 views

Prediction intervals for levels using a VAR model in second differences

Given a VAR model for the second differences of a vector time series, $\Delta^2 y$, how to obtain the one-step-ahead (and possibly $h$-step-ahead) prediction intervals for the series in levels, $y$? ...
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929 views

Time series data prediction with neural network model

I would like to predict stocks of a company for 6 months. I would like to use neural networks for this prediction. Can anyone suggest how many hidden layers and hidden nodes to be used? I have ...
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315 views

Confidence interval for sum of forecasts

I've got two time series, let's say X and Y. They are correlated. I can obtain forcast for X and for Y separatly (I'm using VAR model) and confidence intervals for them. Then I would like to make a ...
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1k views

R: Calculating prediction intervals (95%, seasonal naive and holt winters)

Could somebody explain to me the theory behind how R calculates the 95% prediction intervals for my 12 step ahead forecasts in (1) a seasonal naive model and (2) a Holt-Winters forecast. My code is ...
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63 views

Combining Forecasts: Best Information to Solicit from Forecasters?

Suppose Statistician $m=1$ produces a set of $h$-step-ahead point forecasts $\hat{x}_{t+h|t, 1}$ of $x_{t+h}$ where $x_{t+h} \in [0,1]$. Also, this point forecast could come with: a predictive ...
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493 views

auto.arima and DLM give different values for loglikelihood

I want to estimate an ARIMA model on my timeseries, then represent it in state space format, mainly because it will be more responsive to change in pattern. I used ...
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4k views

Constant in arima model whether to include or exclude?

I have a very basic question on including constant in Arima models. I'll illustrate this by an example. I have the following ACF and PACF of a weekly time series that is differenced at lag 1 (trend) ...
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542 views

Online time series forecasting with DLM

I have estimated a univariate time series model, consisting of a random walk and an AR component. Now the goal is to make forecast about a couple of steps ahead as new data comes in, in an online ...
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1answer
146 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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1k views

How to forecast (extrapolate) within a (B-)Spline setting

Suppose I observe a random variable $Y$ for a co-variable $p\in\{70,90,100,...,170\}$. My goal is create a forecast of $\mathbb{E}(Y)$ for $p\in\{50,70,...,350\}$, i.e., a wider range of $p$ as ...
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92 views

Simple ways to forecast US GDP

Forecasting US GDP sure is hard, even the Fed's FRB/US gets it wrong. I am an undergrad doing a US GDP forecasting project, and was wondering if there were simpler ways to do so and produce decent ...
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243 views

How to extrapolate future probability density functions if you have a time series of them as input?

This is my current situation: I am given an observations vector $\textbf{X}$ of continuous variables with a time component $T$ (not equallly distanced). My supervisor approximates densities with ...
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353 views

Time Series using STS( Basic Structural Model)

I am using Basic Structs to forecast my time series. My forecast is exactly overlapping my data. I am sure no model can predict with 100% accuracy. I know I am missing something, can someone point me ...
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145 views

How to evaluate a Bayesian forecast?

Suppose that I have a predictive posterior, which is an attempt to predict some one-step ahead forecasted value $\hat{y}_{T+1}$. How do I assess if my posterior has done a good job or not? If we had ...
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4k views

Training/ Test Data with Time Series Model — Forecast with Training Model, or with Model based on Full Data?

Okay, I have a couple books on time series forecasting, but perhaps I need to read a couple more. Here's my question. You want to be able to validate a forecasting model. So you split the data into "...
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530 views

Forecast of spot electricity prices

I recently started a job in power trading. But due to a sudden change in employment I am required to work on econometric models to gauge the supply and demand side of national power markets. So ...
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4k views

auto.arima and Arima (forecast package)

I am facing a strange issue with the auto.arima() function. On a dataset named data, I run the following code ...
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715 views

Regression model for predicting life expectancy

I have average life expectancy at birth data for an 8 year period and I would like to use that 8 year period to predict the trend for average life expectancy for the next 5 years. I would then like to ...
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275 views

Why only full ARIMA models in auto.arima?

It seems that the auto.arima function in the "forecast" package in R only considers full ARIMA models. By "full" I mean that if an AR lag $k$ is included, AR lag $j$...
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943 views

Panel data forecasting from Arellano-Bond GMM estimation

I want to come up with predictions of final energy demand per capita (fe) for a panel of countries. Explanatory variables are GDP per capita (gdp) and population density (pop) -- all variables are ...
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150 views

Ensemble model performs better with worse performing consitutent models?

I have a forecast model I am developing that uses some very unreliable input data, missing data (due to sensors or comms failures) is the rule, not an exception. The quantity being forecast is a daily ...
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991 views

Forecasting call volumes over short intervals using R

I am trying to do a basic forecast of call volumes using the forecast library for R. I am not having too much trouble forecasting on a daily or monthly interval, however when I try to forecast on an ...
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3k views

Forecasting daily data with trend, yearly, day of the week, and moving holiday effects

I'm expanding a question I posed earlier because I think it was lacking detail. I'm attempting to forecast daily demand for a restaurant that sells take away food, primarily to office workers on ...
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295 views

are there any nonparametric forecasting methods?

Are there any good statistical non-parametric forecasting methods besides machine learning methods like neural networks/decision trees etc. for time series analysis ? If so, are there any R packages ...
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926 views

Overlap in time series training sets

I have a time series prediction problem where the aim is to forecast the average value of $y_t$ over the next $T$ periods, given all the information available up to point $t$. For example, I want to ...
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207 views

My prediction errors are correlated. Now what?

This is partly an R question and partly a stats question: I am trying to do batch forecasts using the auto.arima function from the forecast package. I have over 1000 items to forecast so doing it by ...
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1k views

Updating ARIMA model

My question is about updating the parameters of a regression with ARIMA errors model as new (monthly) data becomes available each month. Similar question were asked here before: Updating ARIMA ...
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72 views

Predict binary occupancy vector from history of vectors

I have a set of binary vectors where each vector represents one day of occupancy in a house and consists of 48 elements (each element for 30 minutes of the day). Each element can be 1 meaning that ...
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76 views

Forecast pricing data with lead times?

I have some "big data" containing event pricing information with 45 days of lead time quotes up to the day before the event. Thus, it's structured like so: -45 100.00 -44 120.00 ... -1 110.00 We ...
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583 views

Comparing non-nested models with out of sample likelihood

I recently read a paper in which the authors claim that in order to compare the forecasting performance of two non-nested models, models A and B, a valid procedure is to fit models A and B on the same ...
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66 views

Forecasting & social influence data/experiment - Seeking research strategies

In my experiment, individuals assign probabilities to the likelihood of future events, and update their forecasts as frequently as they like. Most questions stay open (receiving new forecasts) for ...
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103 views

Model from $\hat{Y}$s or model from residuals?

This is for modeling revenue by looking at historical data. I am trying to estimate a curve where $x$ = Fiscal Year Quarter and $Y$ = % of Revenue for lifetime of a bid(sale/opportunity/whatever ...
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281 views

Support vector machines and Granger causality

I was wondering if Granger causality would be an efficient tool for searching for relevant input data for an SVM system. For example if I want to forecast SP 500 returns, I could put in my input data ...
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833 views

Time Series Decomposition : Box Cox for Additive Decomp

Coming from basically no time series back ground, this is likely a simple question, but what is the relationship between "being able to" use an additive decomposition of a series into seasonal, trend ...
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101 views

Cyclostationary time series

http://en.wikipedia.org/wiki/Cyclostationary_process What are the methods in modelling and forecasting such time series? It is mentioned in the link above that there is a deterministic approach to ...
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1answer
76 views

Forecasting with after x lags values

I like to build a forecasting model where am allowed to use only l lagged values. That means the model should forecast only l lagged values like $y_{t}$ can be only predicted using values $y_{t-l}$, $...
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1answer
783 views

Restricting a set of predictions to a range of values of non-negative numbers

I am not even sure how to even phrase this question so if anyone could help that would be great. I am analyzing facebook activity and I wish to predict a particular activity (comments, for instance). ...
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29 views

Large dataset of many short time series: what model to use for forecasting a new time series not in the data?

Problem statement Consider this hypothetical but hopefully practical example: You have a dataset consisting of home electricity usage for 1,000 homes in a city. For each home, you have a time series ...
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27 views

Univariate, Multivariate, Cross-Sectional, Repeated/Pooled Cross-Sectional, Panel or Longitudinal Analysis?

I am wondering if there are formal definition that will help distinguish between univariate, multivariate, cross-sectional, repeated/pooled cross-sectional, panel and longitudinal analysis? ...
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24 views

Difference between forecasting and predicting in statsmodels SARIMAX

I am using SARIMAX model from the statsmodels library to predict(forecast) future values in a time-series. The library contains four methods: predict(), get_predictions(), forecast(), get forecast(). ...
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1answer
19 views

Prediction model of test scores based on subjective assessment

I am trying to build a model where I want to measure the accuracy with which supervisors can predict the outcome of test taker's scores. For example, supervisors rate test taker's subjectively before ...
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0answers
20 views

Forecasting with extrenal regressors in R using RUGARCH

I am struggling to find the solution of my problem, I want to model the volatility of the DAX index using some explanatory variables to do so. I am using the rugarch packed and I model the series has ...
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31 views

Forecasting with VAR model

Suppose we want to build a VAR model with two non stationary historical series $\{X_t\}$ and $\{Y_t\}$ . Let us further suppose that in orded to get stationarity I should tranform the series ...

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