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Questions tagged [forecasting]

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

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1answer
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Predictability of a time series

Say we are given a time series $(x_t)_{t \in P}$ where $P$ is the index set of past observations (train set). Imagine that we have built a model for our data and now want to assess predictability of ...
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1answer
30 views

Principles behind time-series forecasting intervals

So, this is truly a bit of a general question, but I am not aware of the guiding principles (if there are any) behind forecasting intervals. For whatever time-series model one might be using, whether ...
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1answer
37 views

ARIMAX - predict

I have the monthly number of patients in a psychiatric facility from Jan 2010 to Dec 2018 - the data shows a seasonal pattern. I want to forecast the number of patients in the facility from Jan 2019 ...
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1answer
39 views

How to fit a stepwise regression with ARIMA errors using Arima function in R?

I am fitting a regression model with ARIMA errors in R using the Arima function from the ...
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Forecasts are at a different level to base in hts/combinef forecast

I am forecasting a multi-level hierarchy of smoothed series using the hts forecasting package. Some series have forecasts at significantly different levels to the input series. As pictured below, ...
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Observation Operator - Data Assimilation

In data assimilation, one assumes the existence of a observation operator $\mathcal{H}$ that maps the model-state vector $\bf{x_b}$ to $ \bf{y_b}$ (the model-equivalent of the observations $\bf{y_o}$) ...
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Which are the benefits of using ARIMA vs LSTM for time series forecasting? [duplicate]

I have already read this question: https://datascience.stackexchange.com/questions/12721/time-series-prediction-using-arima-vs-lstm but I want to know in which circumstances is better ARIMA and in ...
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1answer
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+200

Strategies for predicting 100 binary choices given the previous 100

Background As an experimental psychologist, I've long had an interest in binary decision-making tasks. Typically, in such a task, I manipulate a few properties of some hypothetical or real decision, ...
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Historical average with exponential smoothing model [duplicate]

This topic similar with this one R Time Series Analysis forecast result always remains same But I perfrom exponential smoothing model in R. ...
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1answer
15 views

How to calculate price prediction model accuracy from metrics such as MAE and MSE

I am new to both statistics and machine learning in general. I've tried to construct a price prediction model using the RNN-LSTM architecture. For this problem I have a dataset of one-minute closing ...
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1answer
25 views

AIC values for auto.arima [closed]

I have a problem with identifying why auto.arima suggest specific coefficients. I have time series with multiple seasonalities and I am trying to forecast future values using STL+ARIMA. I have been ...
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18 views

With omitted variables is OLS estimator still the best linear predictor?

Suppose the true model is $$y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \epsilon$$ where $x_1$ and $x_2$ are correlated and $\epsilon$ is white noise. I omit variable $x_2$ and apply OLS to estimate $...
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Prediction intervals for THieF

I would like to add prediction intervals to a temporal aggregation using the thief package. Can someone point out either how to automatically plot prediction ...
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Predict volatility for 20 days after adjusting the GARCH (1,1) Model [closed]

i can't find the right code to predict the volatility for 20 days. What's the right code for fGARCH? Thanks for the help :-) i used this here: library(fGarch) predict(fit,n.ahead = 20) But the ...
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Calculating probability of voting results from small sample aize

2 million people voted in a poll for their favourite song. 35,000 votes have been counted so far, of which: 575 votes are for Song A 466 votes are for Song B 393 votes are for Song C (And the ...
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1answer
216 views

Which estimation technique minimizes the MAPE?

Suppose we have two estimation techniques: Linear Least Squares, which aims to minimize squared residuals Least Absolute Deviation, which aims to minimize absolute residuals We have a model, which ...
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2answers
50 views

Forecasting the number of visitors in each hotel in a city

I am looking for some suggestion on what a good approach would be for the following forecasting problem. Problem statement: There are 100 hotels in a city and I have the monthly data on the total ...
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2answers
81 views

machine learning algorithms (Xgb, LSTM, others) for time series forecasting

I have seen many kernels that are using machine learning algorithms (Xgb, LSTM, others) on time series forecasting. A time series data typically has trend and seasonal components. In general my ...
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Out-of-sample predictive checks for Bayesian TVP models

Comparatively new to Bayesian econometrics so apologies if this is a silly question. I am running a time-varying parameter regression where the parameters are estimated as in Primiceri (2005). My ...
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2answers
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How to study auto-correlation of time series when shocks are present?

The time series I want to model has several shocks due to law changes. Basically, I do not have a lot of data that isn't impacted by these shocks/shifts/pulses. Now, I want to study the ACF and PACF ...
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41 views

Time series model for demand forecasting?

I have a time series $Y_t$ (example:university applications received in a certain month) which I want to forecast. I have another time series $X_t$ and I know that $Y_t$ is related to past lags of $...
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10 views

How to choose the best parameter for the LINEX loss function?

I am using a LINEX loss function to evaluate my forecast. What procedure should I follow to find the best $\alpha$ parameter? LINEX function: $$L(e) = \exp(\alpha e) - \alpha e - 1$$ Where $e$ is the ...
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45 views

ARIMA model with measurement error

I'm interested in forecasting a time series $m_t$ which is contaminated with measurement error $e_t$, so the observed time series is $y_t = m_t + e_t$. I can suppose $m_t$ and $e_t$ are independent ...
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ARMA process forecasts and maximum likelihood parameters

I have some trouble understanding the forecasting/inference process of ARMA models. From Hamilton (which I am reading now), we can obtain forecasts at $Y$ from any linear process with r.v. values $X$...
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1answer
18 views

LSTM - random and always-different time between data measurements?

I am working with a time series problem where the time between two data measurements is random, and I am trying, without luck, to find an LSTM architecture that can handle this. A very simplified ...
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2answers
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Variance inhomogeneity in time series when forecasting

I am using a time series for monthly temperatures to predict future temperatures. To this I am using the seasonal ARIMA model and Holt Winters forecast, and my results seems fine. However, my data ...
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0answers
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Help in structuring problem to forecast end of testing

I need assistance, please, in structuring the following problem. (I can use the World-Wide Help Manual to do the math and the calculations, I need help in setting up the structure of the analysis). ...
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40 views

Programming for hybrid models of arima-ann and arima-svm time series foercasting in rstudio

i am doing my mphil thesis on hybrid modeling of arima-ann and arima-svm for time series forecasting following the G.Peter Zhang's research paper (https://www.sciencedirect.com/science/article/pii/...
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28 views

Using ecmpredict in R to forecast from an ECM

I have fit an ECM model to my data using the ecm function, which is part of the ecm package. I would now like to use ecmpredict to predict/forecast future values of my target variable. The function ...
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25 views

Forecasting Short Time-Series with other time-series

This is sort of related to a previous question, but now I don't have the requirement of generating customer-level forecasts. I've acquired a set of card customers every month for the last 3 years. I ...
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23 views

Programming for SVM or SVR time series foercasting in rstudio

is there any programming codes for SVM for time series forecasting like neural network has build in function of nnetar in forecast package and we can also do it from caret package. if not, then how ...
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2answers
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Compute forecasts and 90% forecast intervals for ARIMA(p,1,q) models

Consider the two models (ARIMA(1,1,0) and (ARIMA(0,1,1)):
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Penalized regressions with forecast package and ets in R

Is there a way within the forecast package and ets to remap or penalize residuals based on some user defined function? E.g. If one wanted to impose a skew in error minimization, is this possible? ...
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1answer
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How to perform 1 step ahead forecasts with a VAR function [closed]

Say I am given the parameters of a VAR (2) function with two variables. How would I use this information to perform a 1 step ahead forecast? Example of what I would have is... $A_t= 1.5 - 0.5A_{t-1} ...
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1answer
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What is multi-step time series forecasting?

Disclaimer: New to this field I am researching ways to forecast a given time-series. So, I understand what Univariate and ...
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model tuning for Holt function

I was working with the Holt function from "aTSA" package, which uses default values of ...
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0answers
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How to obtain an HP filter trend less susceptible to end-point bias?

My understanding is that if a series is stationary, using the HP filter on it does not introduce cyclical artifacts. Also, it has been proposed that in order to overcome the end-point bias one can use ...
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Returns forecast back to closing price?

I'm working with log returns. I've selected an ARMA-GARCH for mean and volatility forecasting and I would like to get the forecasted confidence intervals and plot expressed in terms of the closing ...
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1answer
46 views

Time series: group and then forecast, or forecast and then group

Let's say we want to forecast revenue by month for the next 12 months, and we have daily revenue data for the last 3 years. We could then group this data by month, train our model using revenue by ...
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Testing forecast accuracy in Excel [duplicate]

I'm looking for some advice on how to determine how accurate a forecast is. Basically, what I have is 23 years of competitive results for a particular sport (10000+ matches). The matches are broken ...
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1answer
28 views

Stationarize count based time series data

I have a count based time series sequence with lot of 0s. Usually to achieve stationarity we can do the following transform: ...
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42 views

Time series forecasting does not work on unseen/extreme values during validation

I have a conceptual question and not sure what would be the appropriate solution. I have run a time series forecast using arima methodology. I had several years of data that I used and split my data ...
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0answers
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What is the relationship between the prediction interval of an ARIMA(p,d,q) and the prediction interval of the original variable

The title may be enough, I want to know what is the relationship between the prediction interval of an ARIMA(p,d,q) and the prediction interval of the original variable. Lets say that d = 1, so that I ...
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1answer
38 views

Understanding Intuition for ETS Damping Selection via AIC/BIC

I'm trying to understand how ETS selects whether to use a damped model via information criteria (I'm not sure which of AIC, AICc or BIC are used). I have a time series and I'm comparing two ETS ...
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2answers
103 views

Timeseries with multiplicative noise in Stan

Say we have a monthly time series $y_t \geq 0$ dominated by seasonality, where the absolute differences from year to year are much smaller during low season. To avoid negative values and capture the ...
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1answer
43 views

Forecast for ARIMA in R doesn't seem to fit

I've been trying to implement some ARIMA modelling of data in R (haven't been using R for long so not sure how well this is done), using the forecast library, but the forecasting part itself doesn't ...
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0answers
42 views

Combinef function in R HTS pakage

First I would like to thank R people & package developpers for making available such a collection of great tools. My question is about forecasts reconciliation, as I find huge differences ...
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1answer
45 views

Forecasting algorithms for incomplete time series data [duplicate]

I want to forecast the demand of each SKU in my warehouse every week from the history transaction that I have collected. The data contains brand, product type, SKU, quantity, date(per day), price. But ...
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1answer
27 views

Does forecasting with ARIMA lose non-stationary components?

Suppose I have a time series $Y$. I have read that an ARIMA model consists as an ARMA model of a stationarized version of $Y$. If I try to predict $n$ ticks ahead with an ARIMA forecast model (with $...