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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1answer
15 views

Prewhitening with seasonal response and non-seasonal independent variable

I'm working to develop a forecasting model for a quarterly seasonal variable (quarterly estimated individual income tax payments) using several candidates for non-seasonal independent variables (...
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1answer
305 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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46 views

Short term(10 mins period) forecast using ARIMA

I have 14 months(01/07/2018 to 30/08/2019) of one minute data, which I have aggregated to 10 mins block. So I have a data of dimension "61056 * 350". From this I am using 12 months of data to train ...
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7 views

how to estimate hyper-parameters when cross-validating time series forecasting?

I want to evaluate several forecasting methods on the taylor time series using cross-validation. How do I go about selecting the hyper-parameters for the methods? ...
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0answers
21 views

How does BATS model work?

I am using BATS on a univariate time series model. I have observed strange behaviour. I have data from 2016 to till date (weekly level). If actual are considered from 2016 to 2019 May, I have used ...
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0answers
10 views

Is it necessarily incorrect to randomize train-test split for a time series random forest model?

As part of some preliminary research, I'm experimenting with a random forest classification model for predicting whether the S&P 500 will be higher or lower at tomorrow's close versus today's ...
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7 views

RNN (LSTM) training on multiple time series

Regarding RNN training, We feed network a network -> point by point from the same time series (or image or smth else). When we “switch from one time series to another”, what should be done or how ...
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1answer
18 views

Expected value of an AR(1)

Consider the AR(1) model y_t=α+βy_(t-1)+e_t Where the errors are white noise N(0, 1). What is the expected value of y_t.
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2answers
992 views

When is it appropriate to use an improper scoring rule?

Merkle & Steyvers (2013) write: To formally define a proper scoring rule, let $f$ be a probabilistic forecast of a Bernoulli trial $d$ with true success probability $p$. Proper scoring ...
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1answer
38 views

Is this time series stationary? What would be your approach to forecasting it?

I've been working on the time series prediction of a signal and came across a small misunderstanding. The signal is depicted below: Apparently it looks like there are several stationary local areas ...
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1answer
23 views

why isn't the tscv function allowing for step-size other than 1?

the tsCV function from the forecast package allows for forecasting using a rolling forecast window. Why is it that the step-size is always 1 sample ahead, and the ...
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0answers
26 views

Is it possible to bootstrap a Diebold-Mariano test?

I am currently working on a small project where I want to use a (two-sided) 1-step ahead ($h=1$) Diebold Mariano test to compare forecast losses for different realized measures calculated on time-...
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1answer
73 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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0answers
9 views

extracting seasonality - using fourier transform vs. learning the coefficients of fourier terms

I'm following hyndman's advice for using fourier terms when fitting a linear regression model to the taylor time-series (with the very long seasonality of 336). My ...
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0answers
14 views

Croston's Method (forecast)

I ran Croston's Method on this data(Sales) with h=3 ...
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0answers
4 views

How can I accomodate the forecast series to separately fit and predict workdays and weekends?

I'm using the forecast R package. I have the same dilemma as How to forecast daily time series with weekends and holidays?. I understand that the answers consensus ...
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0answers
15 views

Sparse time series of non negative monthly defect percentage [closed]

I want to forecast monthly (5th business day of month, no timestamp) defect percentages for multiple states of USA. Data is sparse (with many zeros, not geospatial) and could be any percentage between ...
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0answers
4k views

Demand Forecasting Models

I want to forecast demand of various products using time series data of 2 years (using loops on products in R), frequency is daily and demand is to be forecasted for next 90 days I have used the ...
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16 views

Forecasting based on previous realizations

A given process has multiple variables moving to a target point. I can collect data of several realizations of this process. What forecasts methods exist for this? I'm familiar with exponential ...
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0answers
6 views

Neural Networks and a catalogue of the number of floating point operations in various types of statistical and time series models

I recently came across this paper Green AI. In the paper they discussed using floating point operation (FPO) count during testing and implementation of Neural Networks (NNs) to compare the ...
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1answer
645 views

Python ARIMA generates different predictions than SARIMAX for same orders

I was under the impression that Python Statsmodels SARIMAX with seasonal order parameters set to 0 will generate the same forecasts as ARIMA. But apparently the forecasts are wildly different. What ...
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1answer
346 views
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2answers
337 views

Residual autocorrelation and forecasting

My residuals are autocorrelated. Will this be a problem if I want to use the time series to do forecasting?
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2answers
30 views

Predictive Modelling to predict next period sales

I have a Customer data (20 periods) which contains Sales of each customer by Period P1,P2,P3 ... P20) (each period =14 days) Is there anyway to predict P21 period ? Can I used ARIMA or ETS keeping ...
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2answers
134 views

ARIMA (Statistical approach) Vs Regression approach to predict values

I have sales data of my organization for last 3 years and based on that I have to forecast Budget amount for next Fiscal Year. Sales data is available across different Geographies for various Product ...
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0answers
69 views

Does smoothed data work better for time series forecasting with LSTMs?

I am training a 3-layer LSTM on time series data ($10^6$ training samples) to predict the next point in the time series, where there is no seasonality and the time series has been made stationary (...
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0answers
25 views

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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1answer
22 views

tsCV auto.arima with xreg results in NAs

I'm using the tsCV function from the forecast package in R. I'm comparing arima with and without xreg. Without xreg I have no problems. But when I add xreg to the tsCV function I get a matrix of NAs. ...
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0answers
8 views

Uncertainty of additively combined models

I'm dealing with sales forecasts (time-series with few data points) for a company on a department level (one company consists out of multiple departments). My approach is it to build a time-series ...
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4answers
69 views

How to predict how the time series behaves in the future?

I was wondering how a statistician estimates how a time series continues. Namely, which is more accurate way to estimate the series? Draw a line between start and end points of the time series data ...
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25 views

Forecasting for Regression with sARIMA errors only needing new xreg's

I would like to get a better sense of why we only need new exogenous regressor data for forecasting Regression with sARIMA errors to new time periods, and not new time series data. Specifically what ...
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1answer
138 views

K in Fourier series - How to find value of K to use it in ARIMA?

I am using the forecast library for doing some time series forecasting. I need to forecast number of sold items. I am planning to add holidays as xreg in auto.arima. The holiday will be a 0/1 list, ...
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1answer
1k views

Usage of tsclean() in time series data

Consider the scenario, where I have many time series data. I have to make predictions for all.I made a ts object out the data. The data may contain outliers. I am not sure of it. But I always pass ...
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0answers
18 views

Narrowing down model for 2-independent variable time series forecasting

I've got a project I'd like to start working on and it roughly goes like this: I have past time series data for thousands of products that a company produces. There is a high weekly and yearly ...
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0answers
20 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 ...
2
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1answer
136 views

Flat forecast of trended time series data in r

I have a monthly time series of online visits for last 3 years starting from Jan 2016 to Dec 2018 and need to forecast for 2019. The data clearly has an upward trend although no seasonal lags ...
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1answer
1k views

how to help the tree-based model extrapolate?

The following example borrow from forecastxgb author's blog, the tree-based model can't extrapolate in it's nature, but there are definitely some method to combine the benefit of tree model (...
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2answers
2k views

Determine best ARIMA model with AICc and RMSE

I have done a training set to fit different ARIMA models and then a test set to assess their performance (with R). From what I understood, I can use the AICc to determine the best model by choosing ...
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0answers
20 views

ML preprocess to achieve stationarity

I would like to use Machine learning models on top of multivariate time series data to forecast long horizons (for example 400 items and their historical sales in the last year & content features)...
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1answer
247 views

Suggestions for Neural Network Structure for Time-Series prediction with constant covariates

I've been working on a time series prediction problem and wondered if someone has run across a similar problem structure & can make a suggestion on how to structure the training data, network, or ...
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1answer
42 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 ...
1
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1answer
75 views

book recommendation: statistics (time-series) for small samples [closed]

I was hoping someone could recommend a good book on statistical methods for time-series on small data samples. We are really looking to do some forecasting/extrapolation given 13 years of previous ...
2
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1answer
213 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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0answers
20 views

weekly forecasting

I am building a weekly forecasting model using dynamic harmonic regression model with Arima errors and Fourier terms. My understanding is that Fourier terms take care of seasonality of any length ...
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0answers
25 views

Capturing cycles in time series with Fourier

In time series regression with Fourier, Fourier terms are limited to maximum half of the frequency of time series to capture seasonality. my question is: Can we extend Fourier terms beyond half of ...
5
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1answer
448 views

What is the difference between ARMA+Fourier and TBATS model?

I am just wondering that, in terms of the multi-seasonal time series forecast, what is the difference between using auto.arima find the ARMA order, then fit ...
2
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1answer
27 views

time series forecasting - predicting the next 24 hours

I have much the same problem as predict-the-next-24-hours, I have several years of hourly data of demand, and I would like to predict the next 24 hours. Ignoring the multi-seasonality issues - is it ...
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1answer
17 views

WMAPE / WAPE for the evaluation of time series with positive and negative values

I have a time series y that has both positive and negative that I want to predict. For the prediction I normalize the values to a range between 0 and 1. If I give ...
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0answers
23 views

Prediction interval for multi step forecast

If I have a multi-step forecast of some timeseries, where the model is some auto-regressive function y[t] = f(y[t-1],x1[t],x2[t]) is it a valide approach to use sections of historical data to train ...
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0answers
34 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 ...