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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58 views

What methods are available for forecasting with a sample of the data

In predictive analytics, specifically forecasting, what methods are available for getting the same predictive accuracy with $n$ (a sample of the data) which would be achievable with $N$ (all of the ...
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1answer
67 views

Why aren't my variables correlated?

I am currently doing a project on Load forecasting and it is known that in my country the temperature effects the load. I have hourly readings of Load and Temperature from the period between 01-...
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2answers
37 views

Forecasting a Step-Like Time Series

I have an interesting time series dataset. I have monthly data and I would like to forecast the next 12 months of data points. I know the dates at which the dependent variable 'may' change up or ...
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1answer
26 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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1answer
57 views

ARIMA for daily data over 5 years - forecast package

I have a question to auto.arima and seasonality. I have to analyze 39 single datasets which are prices of futures or equities. There are missing data which I replace with na.approx. Then I calculate ...
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1answer
38 views

Predicting n'th percentile [closed]

When we use prediction, we can only say levels. For example: We have 500 sample data for our walking range. And let's say 90 percentile is 16.0 km and 10th percentile is 0.78 km. Well, can only say ...
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1answer
52 views

Combinef in the R HTS package- net aggregation

When using the combinef function in Rob Hyndman's very useful hts library (soon to be in incorporated in the new fable library/tidyverts framework) is it possible to have subtraction at any level? ...
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1answer
24 views

Are conditional mean in an AR(1)-GARCH(1,1) equal for different GARCH(1,1) processes of the same data?

I have created a Markov-Switching GARCH model, where the volatility is defined to be switching between two different GARCH(1,1) processes. The data is assumed to have zero mean, where the data is ...
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10 views

How to forecast the future values ( test data) by making use of the fitted ARIMA model [duplicate]

I am tried as follows But problem is all the fitted values are 39 Data : Test and Train data
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25 views

How do i Remove Differencing applied to a time Series, ARIMA model?

Am trying to forecast using time series method called ARIMA. I have followed steps to build a time series model displayed in the code below. My challenge is on (Merging Actual and Forecast in One ...
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1answer
26 views

When to use Normalized Root-Mean-Squared Error vs Spearman Correlation?

I am doing some Machine Learning experiments with Azure and the graphs that it gives me are measured in Spearman Correlation vs Iteration Number (part of the machine learning) However I was just in ...
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1answer
25 views

Variance of Mean Response at the Mean of the Data

My question concerns the variance of the mean response as outlined in this short article or in this Wikipedia entry. Basically, the variance of the mean response is given by $$\text{Var} \left(\hat{\...
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57 views

Multiple & multi-step time series forecast training data with RNN

I have read a lot of discussion on how to do cross-validation on time series data (e.g. walk forward) but I failed to understand how to properly prepare the training data for multiple time series ...
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1answer
38 views

Fit and Predict Arima in R [closed]

I am trying to fit a Arima model in R with an independent variable (ARIMAX). The model fit data contains both positive and negative numbers. The issue is that after fitting the model, the predict ...
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102 views

Multiple Time series Forecasting Using LSTM in python

Assume I have a m dimensional input feature vector and I would like to perform multiple steps time series forecasting. I have about 500 files which each one is has 100 observations for example ...
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1answer
24 views

How does the sliding window work?

I am not sure how the "Sliding window" method work. Let's assume I have a dataset of number of logins by hour. a) A window of 24hours to predict the next 24h? b) A window of 24h to predict the next ...
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1answer
20 views

Forecasting Process with Limited Historical Data and High Variance

I have a general inquiry regarding a project I am working on. I cannot reveal too much, but I would like to gauge the community here and hopefully be pointed towards the right direction in terms of ...
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2answers
69 views

When forecasting, is it better to remove the outliers or just to transform them?

I am forecasting the number of logins. I have a dataset with the number of logins for each hour. First, I use LOF (local outlier factor) to find the outliers and then I remove them. Second, I use ...
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0answers
33 views

Grouped Time Series Forecast when some of the nodes breakdown

I am attempting to do a grouped time series forecast in R using an ARIMA method at the base nodes. However at such a granular level, a few of nodes do not have enough data and so the auto.arima ...
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27 views

Estimating probability density for forecasts

I've used a handful of algorithms for forecasting future values in a time series. But sometimes what I'm really interested in is not the predicted value, but the probability that some future will be ...
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13 views

Timeseries Forecast with log-normalized and differentiated data

i posted a similar, but more confusion question already. I have a weekly timeseries so far, which looks like this (pls ignore the red line): My original data is (e.g.): ...
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36 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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16 views

Dimension reduction for multivariate spatio-temporal data for hurricanes forecast

I have weather data for the 40 previous years and for each year I have information about the hurricane season (intensity, number of active days, casualties,...). My ultimate goal would be to forecast ...
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2answers
113 views

Removing leading zeros from time series

Currently, I am working with a lot of time series data. A lot of my time series data have a lot of leading zeros. For example, ...
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5 views

How to align predictors with target variable when predictors are sampled at a lower frequency?

I have a set of models I am creating in which the target variable I am forecasting is sampled at a high frequency (daily) but the predictors are all Federal Reserve Macroeconomic variables which are ...
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1answer
44 views

Do we need to stationarize a time series signal when using Kalman filter?

I am working on forecasting the number of logins. I know that before using ARIMA, it is important to remove trend and seasonality. But in the case of Kalman filter, I am not sure. After all it is a ...
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29 views

Using Decomposition to Extrapolate seasonality, cycle and trends of predictors

I'm creating a dynamic regression model in which macroeconomic indicators are predictors/features in the model. I need to forecast these features n-steps into the future. I am planning to decompose ...
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1answer
45 views

How do the forecast intervals from an AR model behave when the time series is inherently stationary?

I'm trying to wrap my head around two contradictory intuitions behind how forecast intervals should behave when we use an AR process to model a stationary time series: (a) On one hand, since the time ...
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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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3answers
218 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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32 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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1answer
57 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
81 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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24 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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1answer
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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0answers
16 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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0answers
23 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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1answer
21 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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30 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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2answers
335 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
66 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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0answers
18 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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0answers
12 views

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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1answer
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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0answers
13 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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0answers
33 views

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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0answers
27 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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0answers
29 views

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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22 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 ...