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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21 views

Opposite results from the residuals and JB result test?

I`m trying to forecast some forex returns of currencies couples. I build up my ARIMA model and test for normality of distribution after the arima is applied. I get different results from the Jarque - ...
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155 views

predictions for AR(1) model

I don't understand how predictions can trace the actual data so closely (see the code below)? Does that make sense? The model is $Y_t = \theta Y_{t-1} + Z_t$ where $Z_t$ is random noise. Hence the ...
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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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37 views

Auto ARIMA model summary interpretation in r

I am new to time series and am trying to forecast a data series in r; which has weekly data. I have a few questions related to the same: While trying to use auto.arima() model, it shows the optimum ...
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8 views

Forecasting project

I have a basic question of forecasting in predictive project. I have to predict wine production in several places. The data that I have is climatic inputs like temperature, humidity... This data is ...
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1answer
64 views

Croston forecasting initialization

I have been working with the Croston method but I have many doubts and I do not know if you can help me! The method says that if demand $x_t$ at period $t$ is $x_t= 0$ then $\hat{z}(t) = \hat{z}(t-1)$ ...
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23 views

How to apply VAR model for a I(1) and other I(0) variable? Objective is to forecast [duplicate]

I am modeling liquidity variable, real money growth with real asset price returns. The former is I(1) and the latter is I(0). The objective is to see the predictive power and forecast. However, can we ...
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35 views

How do you deal with time series with long seasonality period?

I have a few time series (20+) to be jointly forecasted. They are minute-level data with an obvious weekly seasonality. Therefore the seasonality is 7*24*60=10080. Typical time series model can not ...
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15 views

Forecasting with annual variable transformed into monthly

I am forecasting a series Y of monthly frequency and there is a variable X of annual frequency that would help a lot in the forecast if it had the same frequency. I decided to apply the method ...
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22 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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20 views

Some Questions Regarding Very-Short -Term Forecasting

I am dealing with solar power output forecasting, but I am still new to forecasting. I would like to ask a few questions here: It seems that most literature forecasts solar irradiance, while solar ...
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29 views

How to simulate errors? [closed]

When performing forecasting, is it possible to simulate the error term for future events? I have an exponential model that performs reasonably well, however I would like to add some randomness to the ...
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183 views

How to determine $p$ and $q$ in my ARIMA model from these ACF and PACF plots?

I have converted stock price index time series data into stationary series by differencing once, so $d=1$. I also have removed the seasonal component of the data. I want to develop a model for ...
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1answer
64 views

Time series forecasting total sales across stores given known sales for a few stores

I have 3 stores differentiated by the StoreID, and I would like to predict the total sales across stores for the next month. I receive the Early Report of the sale as indicated by the ERSales column. ...
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14 views

Autoregression model prediction

I am using autoregression for predicting next 10 steps ahead, but if I am giving more than 8 input values, it predicts negative value, otherwise the prediction is good. What is the reason behind it? ...
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Reference Request: Forecasting - Mathematically Involved/Rigorous [duplicate]

I'm looking for a book similar to this except more detailed and mathematically involved. I'm a math graduate doing a research project on forecasting electrical demand and have very little background ...
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40 views

Time Series approach

I'm currently working on a prediction problem which deals with prediction of rainfall across US. The data I'm dealing with a time horizon which ranges from 1970 through 2019, at monthly intervals. I ...
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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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Identify when a time series reaches certain value [closed]

I have been using SARIMA to forecast a time series as it has trend and seasonality. Is it possible to identify when the time series would reach a certain value? For example when would the time series ...
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25 views

How does one forecast next point in time series using GAS package in R?

I am using the GAS (Generalised Auto regressive score) package in R in order to forecast a chosen time series. I have read package documentation as well as author published paper and I struggle with ...
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Multistep forecasts in ARFIMA models with Monte-Carlo simulations

I have 3 questions about ARFIMA-* models forecasting. Let's look at standard stationary non-seasonal ARFIMA model representation via coefficients. $$ \left(1 - \sum_{i=1}^p\phi_{i} L\right) \left(1 -...
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21 views

High Correlation after Standardization

I was working on a time series data, where there's a very low relationship between the variables(0.1 to -0.1). After applying standardization to each of the features, half of the it starts to bear ...
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38 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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27 views

statsmodels ARIMA predict function on in sample data points [closed]

I'm trying to use the ARIMA predict function to predict on any test sample--this could include both in sample and out of sample data points, however the frequency will be fixed and equal to the ...
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73 views

Finding Outliers in Resource Allocation Forecast Data

I initially posted this under the DS stack exchange, but after much reading and browsing, I think this is the right place for this question. I'm a workforce analyst at a large retail company, I own ...
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Forecasting consumption data

I am working on consumption expenditure data for 5-6 different years but there are 5 years of missing values in the in between those years. For example, I have data for 2004-05 and 2009-10, with 5 ...
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2answers
102 views

Time Series Forecast with Neural Networks blows up

I programmed a feedforward neural network for forecasting a time series, but the forecast is not stable and reasonable. I used a non-seasonal lag of 3, hence produced a gliding window of 3 as input ...
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20 views

How can I generate a time series with autocorrelation at lags other than 1?

How can I generate a time series that has autocorrelation at a certain lag, but only that lag and nothing else?
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11 views

Multivariate normal error with autocorrelation in second dimension

I am trying to forecast my model, but am unsure how to so in terms of error distribution (using mvrnorm). The model itself essentially estimates numbers over time and state (a matrix time x state). ...
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22 views

How to choose drivers for forecasts based on vector autoregression

as mentioned in the title my question is how to choose from a large set of time series the best Driver for a forecast based on vector autoregression. I am sure that this question is very general. I ...
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20 views

Predicting multiple time series with trend and seasonality

I have multiple parameters collected from network with regular intervals over time. Sample parameter data as below There are number of such parameters from multiple devices. I need to implement ...
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26 views

Closest ARIMA models to Holt-Winter's Mixed Model and Time Series Decomposition Models

Can you please tell which ARIMA model will be closest to Holt-Winter's mixed model and Time Series Decomposition (additive/multiplicative) models And that ARIMA model maybe used in replacement of the ...
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25 views

Developing an appropriate volatility variable to predict stock returns based on past month

I am doing a project about the predictability of stock returns. I am using following regression model: \begin{equation} r_{t} = \alpha+\beta X_{t-1}+\epsilon_{t}, \end{equation} where $r_{t}$ is the ...
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35 views

What is the use of xreg in auto.arima function?

I am working on predicting the number of customer attending an hospital to perform MR scan per day. I have the daily count of the customers attending the hospital for the last 4 years. But I am not ...
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55 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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80 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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1answer
36 views

Predict song genre using LSTM

I have a dataset of songs based on genres. For example, a song may hold {5, 2, 3} as scores set for Sentimental, Rock and Jazz. In total there are 800 songs sequentially arranged. I want to predict ...
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1answer
44 views

What is the meaning of an autoregressive parameter greater than one? [duplicate]

I have created a AR(2,1,0) model with the first two parameters equal to -1.08 and -0.33. I understand that a autoregressive parameter equal to 1 implies non-stationarity and a random walk process so I'...
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25 views

What is the equaton for ARIMAX(1,1,1) and how can I undifference the 1st differenced data to fit the equation? [duplicate]

I have generated the ARIMAX(1,1,1) model to predict the future Barramundi catch. In this model, there are two exogenous variables (price and streamflow) that affect Barramundi catch. I have used 1st ...
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Pricing transfer prices for oil hub? 390 Days of prices given

Need some input in how to attack this problem. Given are 8 timeseries: UK Oil price, Delivery Quarter 1 2020 UK Oil price, Delivery Quarter 2 2020 UK Oil price, Delivery Quarter 3 2020 UK Oil price, ...
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3answers
59 views

Differencing and trend in time series forecasting

I understand that a time series is differenced to remove trend. But if trend can be modeled for forecasting purposes then why difference a time series at all?
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145 views

What is the difference between an accuracy measure and an error metric?

The two concepts are distinct in measure theory. Nonetheless, moving out from measure theory, the two terms are often used interchangeably. To most forecasters, especially forecast practitioners, they ...
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46 views

How to do location forecasting on Chicago Crime Dataset?

I am using the dataset https://www.kaggle.com/currie32/crimes-in-chicago and given primary type of the crime I want to forecast the next location of crime. What approach should I follow ?
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How to interpret ARIMA(0,1,1)(1,0,0)[12] with drift from R? [duplicate]

The code that Î used to generate ARIMA summary is, arimafore = forecast(auto.arima(sales), h = 24) summary(arimafore) and i got this output Forecast method: ...
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37 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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58 views

Make daily business data stationary for ARIMA

For my master thesis I have a dataset with the daily count of orders from a company over ten years. Naturally this data follows strong seasonality with almost no orders on the weekend. To fit an ARIMA ...
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Is stationarity a requirement when using neural networks for time series forecasting?

I'm getting conflicting information on whether stationarity is a requirement when using neural networks for time series forecasting: In this lecture, the speaker says it isn't a requirement. In this ...
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What is the best model to forecast ACT scores using practice test scores and past student data?

I understand that I may not be asking this question correctly, and would appreciate any feedback possible in order to help set me on the path to figuring this out... I work at a high school where ...
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1answer
253 views

Model for spatiotemporal and discrete variables

I have a situation where I am monitoring events at 50 or so geographical sites in a town and at each of these sites, I am making measurements regarding the count of certain particles (so the ...
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ARIMA predictors - clarification

I'm working on multivariate time series (still), and would like some clarification. I was reading this site: Duke Forecasting and I came across this statement: "We see that the most significant ...