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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Appropriate forecasting methods for only 20 observations [duplicate]

I am trying to forecast the regional GDP growth of our region in the next five years, I only have 20 observations in my data which is yearly, what forecasting model is appropriate? I tried ARIMA in r ...
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Should I scale time-series features for supervised learning classification?

I couldn't find an answer to this in the archives so posting this here. I am currently building out a supervised-learning / classification pipeline for time series forecasting (e.g. predicting the ...
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Time Series vs. Queueing Models

Generally speaking, queues are modelled using the Poisson Process. Supposedly, this used to model the dynamic nature of queues, arrivals, birth-death and renewals. But just as a basic question: Why ...
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Models for Long-Term Time-Series Forecasting and Pattern Recognition

I'm trying to find a solution for long-term electricity hourly prices forecasting. Explaining simply, I have some data from 2018 - 2021 containing Demand, Renewable Generation, Hydropower Generation, ...
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ARIMA (0,0,0) same forecasted values

I am trying to forecast the GRDP growth in the next five years. The data I have is an annual values of GRDP growth from 2001 to 2020. I used the Auto ARIMA in R with the following code: ...
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Splitting data frame into training and test and modelling by ARIMAX for forecasting

Apologies am asking for help on this as I am struggling with getting through the piece of analysis! I am trying to split a data set into training and test and to use ARIMAX to forecast using R ...
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Making Sense of Differences Between Durbin-Watson Test, ACF, and auto.arima

Trying to make sense of my results. I'm trying to assess if an intervention had an effect on infection rates using an interrupted time series design. I have monthly data on infection rates for ...
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Different Methods for Forecasting

Data - Monthly Rainfall of a region for the past 20 years Objective - To Forecast for the next 2 years I am new to time series forecasting and I am looking for suggestions on various methods that I ...
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Predictive or Error Tests for Vector Autoregressive Models (VAR)

I have two questions relating to VAR and would kindly appreciate any assistance/opinion: Question 1: I am having difficulty finding a proper predictive ability test for my VAR model to conclude if my ...
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Unevenly spaced time-series forecasting and anomaly detection for an industrial usecase

I am currently working on a PhD project for a car manufacturing company, which basically consists of creating a predictive maintenance application for the machines that are currently used to fill the ...
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Forecasting in intervals

I have data in the interval of 5 years from 1975 to 2018 e.g. (1975-1979). (1980-1984) so on. I need to forecast the numbers in the future from 2018 to 2030 and 2040 in 5 years intervals also. what is ...
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What is the best way to share the percent error of a forecast to a group of peers?

Let's say that I am trying to roughly forecast the total number of commuting hours for the employees at a client company based on some assumptions (# of business days in a week, # of employees at the ...
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Using Matrix profile for feature of forcasting

I read about Matrix Profile which has two primary components; a distance profile and profile index. It has many application such as finding anomalies. Can matrix profile be used as a feature when ...
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Refit model after Holding Cross Validation

I'm trying to do one step forecasts of US inflation using ARIMA and VAR model. The idea is that for each model I want to do model selection based on Holding Cross Validation (if that's the right term),...
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Analysis of intermittent time series/demand patterns (spare parts)

I am currently familiarizing myself with the world of demand forecasting. More specifically, I'm looking at spare parts forecasting, which means intermittent time series and demand patterns are ...
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Time Series Forecasting Process With Regard to Training and Test Sets

I'm a bit confused about the process order in doing proper time series analysis/forecasting. Is it: Stationary/seasonality checks, do any transformations required Candidate model selection using ACF, ...
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Why ML instead of traditional statistical methods in demand forecasting

As the title implies, I would like to know why would we want to use machine learning instead of traditional statistical methods in demand forecasting. What are ML's strengths over let's say ARIMA in ...
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r HoltWintersresults, interpretation of fitted dataframe

Concerning the fitted values, the help page for the HoltWinters function states that the fitted values are: A multiple time series with one column for the filtered ...
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Can the GARCH model predict other things than only volatility?

I was reading that ARIMA models are used to model the mean whereas GARCH models are usually used to model the conditional variance (i.e. volatility). Is it possible that the GARCH model can somehow be ...
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How to predict with a stateful LSTM the next values

I trained a RNN using LSTM cells. I would like to make a predictions for the next 14 days. I get results that are plausible but after reading various blogs I'm not so sure if I'm doing the right thing....
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Presence of underestimation bias in earnings predictions

I am working on a financial data that entails forecasted revenue a company generates over a fiscal quarter and the actual revenue for that quarter. ...
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Forecasting Peak/Global Maximum from Raw Data

I'm trying to see what methods there are to predict when the data will peak based on raw values, along with how to accomplish it in R. Here's what you can assume... The data has a start and end point....
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What are some deep learning models use in timeseries forecasting that include context from covariates?

I was going through the literature for time-series forecasting using DL and all the methods I read about only use the variable of interest at previous timesteps to predict the same variable at time ...
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what does it mean when keras+tensorflow predicts all the timesteps of the Y dependent variable in an ANN?

From what I understand is that in supervised learning problems there is a dependent variable Y, which I included in my ANN. There is one set of matching predictions for each sample for each Y. The ...
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Driver based forecasting using past distributions

I have reduced my original forecasting problem (Short context : I need to forecast hotel bookings and checkins for the next 3 months. I already have a reasonable forecast for bookings and need to ...
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How is Dowd's (2007) resampling procedure supposed to mitigate the problem of autocorrelated multiple-step-ahead forecasts?

Dowd "Validating multiple‐period density‐forecasting models" (2007) considers evaluation of multiple-step-ahead density forecasts. There is a know problem of dependence between forecast ...
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Hierarchical-Grouped time series with exogenous regressors

I'm trying to build an hierarchical time series forecasting model and avoid building a model for each country and aggregating it. I want to add external regressors (continuous and categorical) with ...
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Model performance in time-series forecasting with some outliers

I'm creating forecasts for products where some of them have large seasonal spike during times like Christmas and/or Easter but relatively low sales volume on other times. For this particular product ...
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Gain of power by a smart choice of goodness of fit test

Suppose one would like to test that a sample of observations comes from Uniform(0,1) distribution. Instead of applying the Kolmogorov-Smirnov test on the sample, one may first apply the inverse CDF (...
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Forecasting a distribution

My current struggle is to forecast the distribution of a given value across the next 360 days. e.g. The context is hotel accomodation bookings. We have a forecast of 100 bookings to be made for time t ...
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Grouped Time Series forecasting with scikit-hts

I am trying to forecast sales for multiple time series I took from kaggle's Store item demand forecasting challenge. It consists of a long format time series for 10 stores and 50 items resulting in ...
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Time series deseasoning

The forecasting methods I am interested in include: croston, naive, seasonal naive, ma, adida, exponential smoothing, TSB intermittent demand method). In case the time series contains a seasonal ...
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R- Times series : should I Remove trend before forecasting

I would like to explore forecast methods (eg croston, naive, adida etc) in R for sales data. From searching relevant tutorials, I can see that they first test for seasonality, remove it, and then ...
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Forecasting time series with multiple seasonaliy by using auto_arima(SARIMAX) and Fourier terms

I am trying to forecast a time series in Python by using auto_arima and adding Fourier terms as exogenous features. The data come from kaggle's Store item demand forecasting challenge. It consists of ...
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Next event prediction - approach

I have a problem that I do not know how to solve reasonably. I need predict date and amount of next (future) order of product. So my data looks like this: ...
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Check residuals---HoltWinters forecasting?

Currently I am forecasting for my master thesis. This forecast has been made with HoltWinters. However the graphic seems to be "weird" to me. Anyone some ideas?
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From the ACF and PACF, what are the non-seasonal and seasonal part of the model here to find an appropriate ARIMA model?

I am working on a monthly average dataset and would like to do forecasting. I had used the codes ndiffs() and nsdiffs() to check ...
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Forecasting Future Values of Time Series [closed]

I am working with the R programming language. In particular, I am using "Markov Switching Models" for the purpose modelling more complicated dataset with varying degrees of volatility. For ...
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Why is ARIMA giving better results when predicting a full year than individual weeks?

I am trying to simulate a system that will predict Covid 19 weekly incidence rates based on previous health records + several environmental conditions. The system would use all the data available from ...
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In-sample forecast accuracy of Beta (Kalman filter)

One can calculate time-varying betas (known from the CAPM) using the Kalman filter. For example, one can calculate the in-sample forecast accuracy using the MAE. $MAE = \frac{1}{T}\sum_{t=1}^T|\hat{R}...
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Forecasting analysis in R [closed]

I am working with a database that contains information about the amount of room reservations on a given farm. I will insert the database below (df1 database) As you ...
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Understanding questions regarding the Kalman filter

I have a few questions about the Kalman filter in R (dlm package): Given the function dlmFilter, there is the output time ...
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Aggregating forecasts and their standard errors over time periods

Good day all, I have an ARIMA model which gives me a weekly forecast and standard error for each period. If I were to aggregate the forecasts into monthly time-windows (for simplicity sake, let's ...
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Metrics of Forecast Accuracy that are "fair" with respect to forecasting difficulty

Let's say we want to evaluate and then compare the accuracy of forecasts across different variables for the same country, or the same variable across different countries. Are there ways to account for ...
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Metrics of Forecast Accuracy if Actuals are close to zero or have large outliers

Some measures of forecast accuracy, such as the mean absolute percentage error (MAPE), are "distorted" or are not defined, it the actual realization of the variable is close to zero, or ...
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can autocorrelation in the residuals impact my predictions?

I'm building a linear regression model to forecast the price of nitrogen fertilzers. My predictors are grain prices (wheat, corn or soybeans) and natural gas prices. When I look at autocorrelation of ...
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Understanding how seasonal terms are calculated in unobserved components model (statsmodels)- python

Context I am running an unobservedcomponents model from statsmodel api in python The data is daily in nature , and these are the parameters I have set for my model: I have a day of week pattern in ...
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44 views

check for seasonality

I am quite new to forecasting and I am currently checking for seasonality. I have already checked for stationarity with the Dickey-Fuller test and didn't find one. I have used seasonal decomposition ...
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How to invert data that was detrended with polynomials?

I am using VAR to model some time series data. To make the data stationary I detrended them with regression with n = 2. I used the function below... ...
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How should we deal with anomalies/structural breaks in the rear end of estimation period when forecasting outside the estimation window?

Suppose that we have successfully identified anomalies present near the end of our model estimation period. Also, suppose that the anomalies are linked to extreme events such as economic crises rather ...

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