Questions tagged [forecasting]

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

1,230 questions with no upvoted or accepted answers
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68 views

Resources for learning the time series stuff they don’t (or didn’t) teach you

I at one point, a long time ago, had two years of graduate econometrics focusing on time series, plus more on micro cross-section techniques. I haven’t made much use of the time-series stuff for a ...
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97 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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Is it possible to reconcile forecasts that are not only at different levels of an organizational hierarchy, but also measured in different units?

Consider the following scenario: I have a top line revenue forecasts in dollars for each of my corporate divisions or business units. I have capacity forecasts for each of my regional manufacturing ...
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438 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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1answer
135 views

Should I avoid mixed ARMA models?

I have hourly demand data for taxi rides that spans several years into the past. I want to use it in order to forecast future demand (for the next day). Robert Nau warns against the usage of a mixed ...
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Seasonal ARIMA- non stationarity after differencing and seasonal differencing

I am working with a seasonal time series, which is initially stationary. After many attempts, the best model that fits the data is an ARIMA(0,1,4)(0,1,1)[12]. However, checking for the stationarity of ...
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70 views

Using the Standard Error of Prediction in the presence of practice effects

I’m wondering about the following hypothetical scenario. There’s a student who previously scored 40% on an examination with a pass mark of 50%, a mean mark of 60%, SD of 10 and a reliability of 0.6. ...
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1answer
485 views

Forecasting with AR(1) and pseudo out-of-sample using R

I'm trying to do Pseudo out-of-sample forecasting using R. And, I also have the following initial data (gdp) ...
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345 views

The Efficient Market Hypothesis and forecastability?

According to Wikipedia: The efficient-market hypothesis (EMH) is a theory in financial economics that states that asset prices fully reflect all available information. A direct implication is that ...
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325 views

Obtaining from scratch the volatility in GARCH model using R?

I'm trying to obtain the same vector of volatility by myself $\sqrt{h_{t|t-1}}$ of a Garch Model, that I obtained "automatically" using the function "ugarchfit" from the package "rugarch". So after ...
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74 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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332 views

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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638 views

Adding noise to time series data to increase training data

I am dealing with a weekly time series forecasting problem and I am currently investigating the use of an LSTM to make a multi-step forecast for a univariate time series. I actually have a ...
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203 views

Does Nickell bias matter in forecasting?

The context is longitudinal data, with $i$ indexing individuals and $t$ indexing time. The goal is predicting $y_{it}$ as a function of lags of $y$ as well as $\mathbf{X}$, which might include lags. ...
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381 views

Assumptions on Neural Networks (NNETAR)

Are there any assumptions that must be covered when fitting an NNETAR model? non-correlation, normality, or something? I've already saw Rob Hyndman post where he says NNETAR doesn´t need stationarity, ...
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45 views

Establishing the minimum required training set size, when cross validating time series data

I want to evaluate and compare how well various models perform with regards to modelling time series data (the data in question is daily revenue). It seems that cross validation error might be a ...
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2k views

How does neural network auto-regression produce multistep forecasts?

I am looking at time series forecasting using neural networks as described in Hyndman and Athanasopoulos. They describe Neural Network Auto-Regression models as non-linear generalizations of AR ...
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243 views

Implementaiton of Continuous Ranked Probability Score (CRPS) when Observation is a Distribution

The most general form of the Continuous Ranked Probability Score (CRPS) is defined as, $\int_{\mathbb{R}} \big( \hat{F}^e(x) - F^0(x)\big)^2dx,$ for some true distribution, $F^0$, and empirical ...
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376 views

Does there exist a variant of ARIMA allowing for weighted samples?

I have a univariate time series exhibiting strong periodicity that I want to forecast, and I plan to use ARIMA. However due to specifics of the prediction task that I'm interested in performing, some ...
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265 views

Tree model does't go well on trend

I am using sales time series data 2011 onwards, to make predictions for upto 2 years. Other than date and holiday related features, i created moving averages, y/y ratios and lags. I also extracted ...
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329 views

How much do the parameters in the Holt-Winters model matter?

When fitting a Holt-Winters model, I usually take the approach of retrospectively "predicting" some known historical values for the series, and optimising the coefficients for the parameters by ...
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1answer
604 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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699 views

Alternatives to Holt-Winters models when the seasonality pattern has changed

I am forecasting a series of daily volumes in terms of units processed for a particular time period (the period around Christmas). Historically, I have used a Holt-Winters model, with the minor ...
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289 views

How to interpreate Reliability Diagrams?

In general the interpretation of reliability diagrams isn't a problem for me, to identify if a forecast is over- or underestimated. But in this dataset which contains different product probabilities ...
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1answer
101 views

How to account for remainder in forecasting?

I've done an STL decomposition of a time series. While trend accounts for major variations, remainder has a "cyclical" setup. I want to know what other forecasting methods I can apply to maximize ...
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297 views

How Negative Binomial Distribution and negative bionomial regression can be used to sales forecast?

My first question here. Due to the improper inventory management we seem to have dispersed sales, and the stores are unable to meet the demand because items are being out of stock. There are so much ...
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177 views

Steps to find optimal transformation for wide-sense stationarity

I've been trying to automate the procedure of choosing the best transformation for a non-stationary process (in R). For lack of a better term, "best transformation" here refers to the quality of ...
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62 views

Is 5 in 100,000 the same as 50 in 1000,000 from a Bayesian perspective?

I am learning about bayesian reasoning and I was having a discussion with a friend about it. My friend asked me this question, which I couldn't answer (this is not a homework, I am an adult learning ...
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44 views

How to include handle days with no delivery?

I am currently trying to forecast the delivery of cash to branches. The problem I face is that there are a few days, as well as most Sundays, in which there are no deliveries. Hence, in the time ...
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22 views

Scaling prediction from VAR model subject to a equality constraint

I have a forecasting problem and already built a decently working VAR model which provides forecasts as $\hat{Y}_{iT}$, for $i = 1,..n$ and $T$ is forecast time period. But now I have an additional ...
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152 views

AIC comparison vs. unit root test for model selection in forecasting

Rob J. Hyndman once wrote in "Why I don't like statistical tests" (emphasis is mine): In forecasting, the only place in which I find testing useful is in determining the order of integration of a ...
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2k views

How to estimate weekly and daily seasonality for data with 15min frequency in Python?

I am relatively new to time series. My goal is to predict a few hours of data, measured every 15min based on three months of observations in Python. I assume I have daily and weekly cycles which I ...
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245 views

Predictability of predictor variables in regression analysis

I have run a multiple linear regression analysis to predict the forecast of demand (in litres) of soft drinks. I have 104 sets of weekly data and my independent variables are feature space (measured ...
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100 views

Is there a general and completely automated (i.e. programmed) solution to modelling and forecasting time series data?

Is there a general and completely automated solution to modelling and forecasting time series data? I think this question is extremely important. If it is not possible, please provide an explanation ...
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25 views

How to make shrinked forecast for the extreme value?

Let me use made-up example: John loves running. He decided to run in his local half-marathon for the first time in his life. He never measured exactly how fast he runs the distance, but while ...
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173 views

VAR model with different time period for each series

I am trying to fit a Vector Autoregression model to forecast GDP growth Rate. I have 2 series, monthly GDP growth rate and a monthly economic indicator. For the monthly GDP growth rate, the latest ...
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1k views

How to select the length of a time series when fitting models for prediction

Let say that one wants to fit a model to a daily financial time series for prediction (e.g. ARIMA, SVM). If data are stationary, ideally the longer the time series, the better. In practice, I don't ...
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619 views

What's an example of a best linear predictor that's not a best predictor?

I just learned about the definition of a best linear predictor found by minimization of variance of $Y$ given $X$, or in other words trying to minimize the variance of $$\mathbb{E}\left(\{Y-\hat{Y}(...
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324 views

Computing Seasonality Index and application to shorter time series

Background: I have an overall time series of close to 3 years of data. I need to forecast for different slices of data. When I slice the data, some slices results in a shorter time series. We go with ...
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373 views

How to calculate prediction intervals based on Chebyshev inequality?

I have recently read the article by Gardner (1988) who proposes Chebyshev inequality-based prediction intervals for forecast: suppose we have a model selected on the usual basis of one-step-ahead ...
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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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185 views

Prediction intervals for forecasts using spectral analysis

I have circadian data which typically have a period of around 24 hours so using spectral analysis seems appropriate. I've used spectrum resampling which is quite robust to changes in period which ...
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249 views

Forecasting demand with out-of-stock data

Usually retailers have a service level that is below 1.0, which means that share of products is out-of-stock some of the time. What is the best practice or possible ways of using out-of-stock data to ...
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5k 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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209 views

Reasons for GLM ('identity') performing better than GLM ('gamma') for predicting a gamma distributed variable?

I am investigating different methods for fitting my target variable (observed wind speed: positive, real, with small values being most probable) using generalized linear modeling (GLM) and - in a ...
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251 views

Box-Jenkins Forecasting With ARIMA(p,d,q) models

I want to check that I understand the general theme of forecasting with ARIMA models using box-jenkins, so I am going to take an example and then proceed from there. We will use $B$ notation for the ...
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283 views

Can Intervention analysis be used to forecast time series

if I have an estimate of the intervention variable from a similarly interrupted time series can it be used to forecast another similar time series after the effect of intervention. For example lets ...
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152 views

Seasonal ARIMA Forecast

I'm studying ARIMA at the moment with application to seasonal data sets. R lets you forecast using selected models but I'm just wondering what formula is used to compute these forecasts. For example, ...
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2k views

How can i do time series forecasting with missing data

I am relatively new to time series forecasting, I have worked previously with continuous data at regular intervals successfully, Now I have a data set with missing values, for example look at the ...
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888 views

Forecastability and Coefficient of Variation

I'm trying to get a sense check here. When determining "forecastability" for sales data, I tend to use the CV. However, this is highly susceptible to seasonality and outliers. As such, I was wondering:...

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