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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12 views

Forecast survey response rate

I am new to Bayesian forecasting and hope you can help me get started with this problem: I need to forecast the likely survey response rate to a paid-for survey Background information: Each person ...
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21 views

What is the coefficients of determination of prediction?

I have never seen this term mentioned before. Yet this study uses it: https://www.econstor.eu/bitstream/10419/204328/1/ifro-wp-2011-12.pdf Is it any different than the typical R^2, i.e. ...
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Time series forecasting: very low loss but prediction is totally off

I just started ML learning using time-series forecasting with LSTM. I am training the model using a dataset of shape (1, 100, 1) (1 batch, 100 steps, univariate timeseries) over and over again (in ...
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22 views

Modelling a race

If we imagine an outdoor race with two obstacles: If the participant fails an obstacle attempt they exit the race Historical data shows that about 50% of participants will fail each obstacle That ...
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1answer
121 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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1answer
363 views

A time series logit model with lagged dependent variable

I have a panel dataset for stocks. My goal is to model and predict if the stock will close positive (1) tomorrow based on today's close (1/0) and other macroeconomic and firm-specific variables.So I ...
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2answers
56 views

VAR models vs univariate models

Suppose I know the true DGP is a VAR(1) process. Instead of fitting a VAR model, I can still fit univariate ARMA models to each of its components. Does anyone know whether it will result in biased ...
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Model Confidence Set by Hansen [closed]

Is anyone familiar with the MCS of Hansen for comparing relative performance of forecasting models (most usually volatility ones)? I have trouble understanding which statistic should one use, since ...
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Paradox in model selection (AIC, BIC, to explain or to predict?)

Having read Galit Shmueli's "To Explain or to Predict" (2010) I am puzzled by an apparent contradiction. There are three premises, AIC- versus BIC-based model choice (end of p. 300 - start of p. 301):...
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Gaussian process with time series

I am trying to apply Gaussian process to estimate the value of a sensor reading. I have the readings of the sensor for few years (hourly paced time series) so my data is an array of two columns the ...
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21 views

Forecasting Prediction in R

Currently, I have a dataset which includes the price of electricity every hour and the demand of electricity every hour. I have then another dataset that only has the demand for electricity every hour ...
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2answers
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Is there a guide for when to implement time series techniques?

I am interested in getting a better sense as to when to use time series techniques. Let's say you have a data set with units sold as the response. Your goal is to predict units sold on any given ...
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The point of VAR conditional forecasts

I wonder what's the point of making conditional forecasts in VARs as in Waggoner, Zha (1998) in favor of forecasting via VARX. What makes it especially dubious is the fact that VAR is estimated step ...
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1answer
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What is the difference between using a time series model and using i.e the naïve approach for forecasting?

I was reading about forecasting at Wikipedia: Forecasting and I noticed that in the publication they separate the Naïve, Average and Drift approach from the Time series methods (which involve AR, MA, ...
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1answer
229 views

Flat forecast of trended time series data in r

I have a monthly time series of online visits for last 3 years starting from Jan 2016 to Dec 2018 and need to forecast for 2019. The data clearly has an upward trend although no seasonal lags ...
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1answer
22 views

Distribution that acts like Poisson/NegBin for small means and like a Normal distribution for large means?

I want to generate a full density probabilistic forecasting model, where I don't know a priori whether the time series I want to model are intermittent or dense. In both cases, the time series is a ...
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16 views

Marketing with Statistics sources

Can you tell me about some books about Marketing topics, using R or Python? I'd like to cover: Cannibalization Models Marketing Mix Models Market Basket Analysis Share of Market Forecast Models
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Interpreting Ljung-Box white-noise test p-value

Good evening all, I am having some trouble understanding the Ljung-Box white-noise test p-value from SAS Forecast. So there are lags where the p-value exceeds 0.05, meaning that we fail to reject the ...
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2k views

Usage of tsclean() in time series data

Consider the scenario, where I have many time series data. I have to make predictions for all.I made a ts object out the data. The data may contain outliers. I am not sure of it. But I always pass ...
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1answer
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Specifying seasonality in a grouped ARIMA model with fable

I'm using Rob Hyndman's groovy new tidyverts family of packages (the replacement for forecast). I was just wondering how you'd specify that the data is seasonal, especially in the presence of groups. ...
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Predictions on transformed series post intervention analysis

I have taken this logged data and performed an intervention analysis: ...
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K in Fourier series - How to find value of K to use it in ARIMA?

I am using the forecast library for doing some time series forecasting. I need to forecast number of sold items. I am planning to add holidays as xreg in auto.arima. The holiday will be a 0/1 list, ...
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Selection Of Model---prediction of cpu usage

Currently I am working on a project and was hoping to ask this question as a sanity check as I am still very new to this area. Currently I am collecting CPU usage values for machines at a 1s interval ...
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What kind of method to use to predict multiple time-series with missing data points and of different lengths

I have to create a model to predict future sales of different restaurants to use in investment decisions. For about 1000 restaurants, I have the following data: Weekly sales (split out over product ...
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Forecasting panel data

I am trying to forecast my dependent variable 9 periods ahead, having a history of 25 years. I have panel data with 34 countries and 25 years for each country – 850 observations in total. Currently ...
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Regarding Hyndman's approach to estimating prediction intervals for forecasts generated by neural networks

I'm currently looking for ways to estimate prediction intervals from an LSTM generated forecast. Several advanced methods are suggested in the literature (e.g. SQF-RNN), but as a first pass, I'm ...
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51 views

Do you clean the data before calculating MASE (Mean Absolute Scaled Error)

The denominator in the MASE calculation for seasonal data is the MAE of the seasonal naive forecast calculated in-sample. Is it common to do imputation before calculating the seasonal naive MAE or ...
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Forecasting R Package Downloads

Disclaimer My ramble posed below may be considered off-topic for CV, but I thought I would at least try to post it here as I am curious as to others' thoughts/opinions, because I have not encountered ...
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Forecast package in R

I have one question which is maybe very simple. So my question is does models from forecast package in R (e.g auto.arima,ets,tbats,nnetar etc) are machine learning models or not?
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1answer
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Akaike information criterion, long time series, and overfitting ARIMA?

I'm trying to forecast a stock index with daily data from 1990 to today (over 7000 data points) with ARIMA, after correlogram, information criterion (prioritizing Akaike) and auto selection (either ...
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40 views

How do I interpret parameter estimates in additive exponential smoothing?

I am trying doing a course on time series forecasting and did a forecast for an airport arrival on SAS forecast studio. Additive exponential smoothing is used for my choice of forecasting. How do I ...
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Forcasting with only total sale of product?

is it possible to make a forecast when you only have the total sale (of last year) per product variant? I would like to make a forecast for the next year 2021. So for example, if last year's sale per ...
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1answer
42 views

Is it appropriate to select an ARIMA model without having statistical significance of all the parameters?

I am trying to identify an ARIMA model for the following time series: According to the ADF test, it is stationary (p-value = 0.0144). When I use the ACF and PACF, both show correlation without a ...
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Predictive maintenance machine learning model to identify a failure before it happens

I have 1000 industrial sensors that send data once a day. The 10 sent parameters along with sensor pass/fail info is stored. The data format is below. If a sensor fails there is no further ...
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1answer
60 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
51 views

Manually replicating an ARIMA forecast

I hate to ask this question but I am going insane and other links haven't solved this problem. I have a seasonal ARIMA with just over two years of weekly data ARIMA(0,1,1)(0,1,1)[52]. It's highly ...
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Time series model for cryptoprice prediction

I am fairly new to the topic of statistics and data science. My first dataset consists out of the BTC prices since 2013 per minute. The second dataset consits out of posts from a social media platform ...
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1answer
351 views

Forecasting energy consumption with ARIMA and regressors

Known data, 4 years of daily energy consumption correlated with temperature, seasonal calendar and holidays. Required forecasting for next days depending on known variables like temperature and ...
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Predict Sales as Counterfactual

Which modelling strategy (time frame, features, modelling technique) would you recommend to forecast 3-month sales for total customer base? At my company, we often analyse the effect of e.g. ...
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Back-transforming forecast points and error bounds from a VAR model applied to alr-transformed compositional time series

The data I have a k=3 compositional time series from which I am trying to forecast future values. The series is compositional in that at each time ...
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What are the one-step time series forecasting methods?

What are the time series forecasting models which purpose is to make just a one-step prediction? How do I statistically validate a time series forecasting model which purpose is to make just a one-...
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Machine learning models for time-series?

I am trying to make prediction of univariate time series with functions from forecast package like: ets,auto.arima and nnetar.During modeling I divide data in traning and test set.So first I traning ...
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1answer
116 views

Incorporate additional information in Stock Forecasting

I am trying to forecast stock of health products. Other than historical stock quantity, I would have some other information, e.g.,: Certain stocks are in compete of each other; Certain stocks are ...
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Time series analysis video resources

I am kinda new in Data Science. My background is in Mathematics. I took some graduate-level statistics courses like the generalized linear model. I am interested to forecast future student enrollment ...
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Time Series Forecasting: If my data is not autocorrelated, does this mean time series forecasting is not appropriate?

I'm new to time series forecasting and I'm finding some of the concepts a little counterintuitive compared to the usual statistical models I use (regression etc). I attempted to do an ARIMA on my ...
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Holt-Winters (or similar exponential smoothing methods) - equivalent to three-standard deviation for forecast variance?

I am trying to implement Heijunka - production levelling - for products at a manufacturer. My feeling is that one way to do this would be to use Holt-Winters estimates of volumes across time, and ...
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51 views

How to forecast low values in data more accurately than the higher values?

I have a scenario where I have to forecast small values in data more accurately than the higher values. I have data set as below ...
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How to reproduce fitted ARIMA(0,1,1) values?

I am using R's forecast package's auto.arima function to forecast the following time series: ...
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Subscription Based Revenue Prediction

My dataset is on revenues from subscription-based (no commitment, can cancel any time). We have people signing up every year, continue paying for a few years and then gradually cancel the subscription....
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What is the difference between ARMA+Fourier and TBATS model?

I am just wondering that, in terms of the multi-seasonal time series forecast, what is the difference between using auto.arima find the ARMA order, then fit ...

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