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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32 views

How to fit a regression model with ARIMA errors on the seasonally adjusted component of a time series (in R)?

I want to do these two things (combined) with a time series T: forecast the seasonally adjusted component of T (STL used for the decomposition) and "add back" the seasonality (I assume that the ...
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1answer
87 views

Is it possible to forecast multivariate time series using exponential smoothing equations? If yes what are those equations?

I know we can forecast univariate time series using different models of exponential smoothing , but am searching for whether same can be extended to multivariate time series and if yes what are those ...
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40 views

Adjustment of the forecast of a time series for the analysis of a system

I have a simulation model of a system which receives a forecast of a time series as input. In my scientific work I would like to examine how the performance of the simulation model behaves in relation ...
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42 views

Demand forecasting

I am forecasting number of phone calls (y) i ll be getting based on the products sold(x) and I am doing the following: (forecasted y) = (y/x averaged for past three weeks) * (forecasted x). 1. Please ...
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36 views

How to measure/rate the effect of a exogenous covariate in a ARIMAX Model?

I have an ARIMA model, I'm trying to figure out how much an external variable (exogenous covariate) could improve the forecast, so I need to "synthesize" a rate that tell me the usefulness (or impact) ...
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48 views

Product mix forecasting method

I have a main segment which includes different products. I have the percentages for each product and two year quarterly data. By using this information I want to forecast next years' percentages. I ...
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1answer
35 views

If I have a time series forecast density that is bi-modal, does that mean that my data is heteroscedastic?

The title pretty much explains it already: If I have enough data points that I can plot my entire forecast density and it ends up looking like this, does it mean that it is heteroscedastic and I ...
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32 views

Is it correct to make a conclusion as to whether a model is best for weekly or daily forecasting by comparing the root mean square errors?

I am performing daily and weekly forecasts for 28 days and 4 weeks respectively. Once I have used the same model to obtain the respective forecasts an root mean square errors (RMSE), I will like to ...
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49 views

Missing as opposed to non existent data in time-series forecasting

Suppose you have a set of observations that occur at regular intervals in time, but containing regular gaps during which there is no data, not because it is hidden or missing, but because the ...
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19 views

Incorporate recent drop in number of units sold in a forecast using exponential smoothing

I'm trying to generate a one-year forecast for the number of units sold by a retail company. I'm using monthly data from 2017 and 2018. The forecast is for 2019, and I'm using the data from the months ...
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60 views

How to train an LSTM model on multiple single-variable time-series data?

I am quite new to the field. I am working on a problem involving time-series forecasting of single variable time-series. Data is collected from the pressure sensor on a patient in hospital. Time ...
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15 views

Does forecast error variance decomposition in which the response variable predominately explains itself imply the model is incorrectly specified?

So I have set up a six variable VAR model in the hope of explain natural gas prices and performed forecast error variance decomposition, however the response variable (natural gas prices) explains ...
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114 views

ARIMA(0, 1, 0) or ARIMA(0, 0, 0) for Stock log-Returns Forecast

I'm trying to forecast the log-returns of Amazon's stocks using the ARIMA model, so I went through the traditional procedure of examining the autocorrelation plot and the partial autocorrelation plot ...
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144 views

Neural Network regression on time series

I want to predict the trend values of a time serie [Y] based on the effect of other 10 input variables which can also have interaction. Since the combination of interaction between the inputs is ...
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21 views

Future event prediction methodology

I have a data set such that each data point is an "event" with features $x_1, x_2, \dots, x_n$ and the year of its occurence $y$. I want to train a forecasting model that predicts when an event of ...
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2answers
171 views

Forecasting daily data with zeros in Python

I'm currently testing some forecasts on daily sales quantities. However, out of ~2000 observations I have 16 zeros. How should I approach this? It's mainly Sundays and holidays that holds zero as ...
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2answers
566 views

Multi-step ahead forecasting with LSTM neural network

I would like to forecast the heat load of a district heating network given its past values, the temperature and the 3-day ahead forecast of the temperature with an LSTM RNN. The data is hourly and I ...
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1answer
217 views

Forecasting recurring orders for an online subscription business using Facebook Prophet and R

I am analyzing data from a subscription model, in which a customer must pay a recurring price at a regular interval (30 days) for access to the product. EDIT -> Direct link to daily data: https://...
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47 views

How do you evaluate bias and/or quality of time-series forecasts

I am working on a financial model that will forecast the revenue a company generates over a fiscal quarter, and I am not sure of the best way to rigorously evaluate the bias in the model. Every day ...
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40 views

How to get quantiles/probabilities of time series forecasts?

my problem is as follows : I am creating demand forecasts for some goods with different methods (ARIMA, ETS,..) The issue is that I would like to forecast the probabilities of those forecasts since ...
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28 views

How to Choose Error Distribution For Time-Series Model

I am modelling a set of time-series, and understand various models (ARIMA, AR, GARCH) allow for the inclusion of non-Gaussian error distributions. I am aware that, after fitting a time-series model, ...
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1answer
29 views

Including regressors to improve forecasts on white noise

I am conducting some time series forecasts using quite limited data, 13 years annually. Basically, I am trying to forecast companies emission totals using historical values. The historical data ...
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42 views

Why am I getting better MAPEs when running an ARIMA model on a non-stationary time series (vs. a stationary one)?

I've been using ARIMA modelling to predict the number of orders a business receives. I have data for 3 years, and the time series shows a strong (uneven) upward trend, with increasing variance over ...
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37 views

Violinplot vs. permutation importance: interpreting differences

I'm analyzing the Titanic dataset, and I've been trying to understand the predictive power of the Age feature relative to passenger survival. My intuition is that younger people had a higher chance to ...
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25 views

Forecasting time series data using EEMD based SVM?

Splitting of Dataset: Dataset = Train1 + Test1 EEMD(Train1) = train1 + test1 I am forecasting on time series data("Dataset") using SVM. First I found the Intrinic Mode Function(IMF) of time series ...
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27 views

Monthly data vs aggregating weekly data to monthly in linear regression

Let's suppose I have weekly data available for making a forecast using a linear regression model. Let's also assume that the weekly forecasts can be aggregated to monthly ones. Would it make sense to ...
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58 views

What values of ARIMA(p,d,q)(P,D,Q)[7] should I use?

I am working on a data consisting of number of customers visiting a clinic for an X-ray scan on the daily basis. I have the data for the last 4 years. I am building a time series model to predict the ...
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58 views

Volatility forecast with GARCH(1,1)

I am having trouble with this question: $Y_t = \sigma_t \epsilon_t$ $\sigma^2_t = 0.003+0.41Y^2_{t-1}+0.53 \sigma^2_{t-1}$ and I am given that $\sigma^2_T = 0.01$ and $Y_T = 0.2$. I am asked to ...
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203 views

Minimum sample size for time series cross-validation (tsCV)

I am doing cross-validation of an autoregressive neural network model and I am using the tsCV function (forecast package) ...
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80 views

Recurrence of $k$-step ahead forecast with ARMA

For brevity, let's consider an AR(1) model, but this question should apply to ARMA(p, q) in general. Assume we are at time $T$ and would like to forecast $k$ steps ahead, $$ X_{T+k} = \phi_0 + \phi_1 ...
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1answer
61 views

Monthly Times Series Modeling Approach

I have a machine learning problem and have been working in Sklearn/Pandas with Python to come up with an accurate model. I find myself deep in a rabbit hole trying to learn the best approach and how ...
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1answer
43 views

Using lagged explanatory variables to forecast future value of depended

Is there a way or method to use older values (lagged) of independent variables with alternative lags to explain current value of dependent variable? For time series specific
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108 views
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19 views

Forecast equation for ARIMA(0,1,q) in R

I am trying to figure out how R computes predictions for a fitted MA process of integration order 1. Consider the following trivial example of fitting an ARIMA(0,1,1) process to random data in R: <...
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113 views

How to add the effect of structual change points (level shift, local time trends, changes in seasonal pulses ) in ARIMA IN PYTHON?

I am working on a time series forecasting problem.As I came to know that I was not considering structural changes and seasonal dummies and was building a simple ARIMA that was causing a very poor fit. ...
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1answer
99 views

Model for forecasting daily page views of a web page in R

I have to forecast daily page views of a web portal. We have the daily page views of the data for the last 2 years. We have to forecast for next 90 days. I am using a multi-seasonal (with season ...
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38 views

Prediction of financial time series

I have several questions. I will split the text up in one high-level description of the goal of my exercise, a detailed description of my potential solution and finally my actual questions. Please ...
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33 views

Finding Probability of a digit given a sequences?

I have a n sequences of numbers ranges from 1 to 4, say sequence s1 = [1,3, 1, 4] and s2 = [2,1,3,4] up to s(n). My question is how can i find probability of a number coming right after a sequence, ...
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61 views

Subsampling as a method for time series train/validation splits

I have a question concerning train-test splits for time series data: Background I have a dataset of sensor data points for 13 month with datapoints measured every 5 minutes which I downsample to ...
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32 views

Quantiles of transformed series

Let's have a time series $S_{n}$ of a same market asset. Let's $R_n = ln(S_n/S_{n-1})$ be an asset returns. So, I could forecast same $\overline{R}_{N+1}, \overline{R}_{N+2}, ... , \overline{R}_{N+j}$,...
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1answer
90 views

Dealing up with collinearity predictors' choice in xreg using auto.arima

I'm trying to do a regression with arima errors in R, with xreg in auto.arima following https://otexts.com/fpp2/ by https://robjhyndman.com/ but I have some questions about the predictors' choice in ...
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1answer
132 views

How can I ensure that my backcasting values are positive in R?

Is there any option in R that could allow me to obtain just positive values in my forecast? I applied the following code for backcasting public expenditure in health quarterly time-series, but I ...
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60 views

(multiple) fractional outcomes & autoregression

Let me start with a broad description of the problem and I will then describe my approach (that might be totally inappropiate). The big goal is to predict the distribution of population of a given age ...
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46 views

Forecasting and prediction intervals for aggregates

I have monthly time series and I need to make predictions for the next quarter. However, for operational reasons, the forecast must be made one month before the quarter. That way, I need, in practice, ...
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86 views

Can a time series model be improved when the residuals are white noise?

For a weekly time series, I have been trying some very basic models to get an understanding of time series analysis. The models I tried so far: naive, seasonal naive, an overall mean model, and a ...
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26 views

Intersecting a linear and exponential with uncertain parameterisations

I'm trying to forecast the point at which two trends will intersect. One is exponential, the other decreasing and linear. In particular, I want to do this with a distribution over the parameters of ...
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1answer
73 views

Asymmetric error measure for forecasts

I am building a model for forecasting some number of activations. My data set has a panel structure. Now, I want to come up with a forecast performance measure to assess the performance of my model ...
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1answer
122 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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106 views

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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33 views

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 ...