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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Time series forecasting with Python

I have the following min-max normalized 2D dataset: The actual dataset has 32 features + y, and several thousand instances (features 1-5 start having different values further down the line). I plan ...
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Sales forecasting

I'm trying to do a sales forecast of an existing product in a new market. I've been doing research and I think new product sales forecasting is the right characterization of the problem. Am I right to ...
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Recommended sales forecasting methods for fast food restaurant [closed]

I'm trying to do a sales forecast for fast food restauarant and I am reading up on various methods for sales forecasting. It's a new product without a test market. It's a bit confusing so I was ...
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how can I use neural network for one day ahead forecasting? [closed]

I have 5 years of data for (1st January 2010 - 31st Dec 2015) and I want to use the neural network to predict 2015 one day ahead. How can I use auto regressive neural network for one day ahead ...
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Should Model Convergence Time Be A Priority for 5-10 year ahead forecasts? [closed]

There is a slew of academic papers that look at basically the same base model while switching optimization algorithms for either its hyperparameter or model parameter tuning. While I acknowledge the ...
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Gap between original data and forecasting time series

I forecasted a set of data, 'ar1.s' from the TSA package, it was meant to be an AR(1) process. The code I used is: ...
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Dynamic Regression vs linear regression oddity

I tried a regression using tslm in R of the "medicaid participation rate" on trend, poverty rate lagged, and a dummy indicating after 2014. The fit is ...
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Choosing proper Frequency argument for time series object

I have a year-long data of an anemometric tower. The variable that I'm interested in is wind speed at 50m. My idea is to train with this data using some models (Arima, Sarima, Holt-Winters, NNETAR, ...
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Auto.arima application in R with sub-daily data - No seasonality given [duplicate]

I'm working on a R code and my aim is to make forecasts with a model chosen by applying auto.arima function on my data. These are recorded every 6 minutes, so we're dealing with sub-daily data. ...
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What is this measure of error called?

I am investigating prediction errors in a context where the errors can be extremely large. Someone advised me that in addition to reporting the mean or median of the absolute errors $$|\hat{x} - x|$$ ...
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Multivariate Time series forecasting- Statistical methods

I was trying to forecast the truck numbers required at each distribution location...for that I was forecasting the shipments(number of units) at each location and dividing it by a factor to get the ...
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Analyze source of forecast error

I have inventory forecasts and inventory actuals for every month for the previous year. Results are around 2% forecast error and I want to know the parameters that cause it (I have many parameters ...
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Ambiguity in definition of Long Term Forecasting

The well known definition of a long term forecasting model is based on the length of the forecast horizon. In case of long term, it is usually agreed to be in the order of years (ahead of the most ...
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Including a “year” effect in an LSTM sequence-to-sequence model for time-series forecasting

I am working on a problem to forecast some climate sensor data. I have data that includes daily sensor measurements from 1980 till present. Now I have written a Sequence-to-Sequence LSTM model that ...
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Best approach to get forecast on multiple features - time dependent dataset

I'm new to this community and I don't know if this is the right place to ask this question, sorry if I'm wrong. It's kind of a subjective question but I have no knowledge about this forecast type, the ...
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Holt-Winters: beta = 0

I have estimated the Holt-Winters exponential smoothing with trend and additive seasonal components. The optimized smoothings parameters that I have obtained are $\alpha =0.41, \beta =0, \gamma =0.47$....
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Does the order matters for an LSTM? [closed]

working on a project where I have many observations with lagged values but the order of all the data is the following X1, X2, X3 , ... , Xn, X1(t-1), X1(t-2), X1(t-n),..., X2(t-1),X2(t-2) and so on, ...
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Expected Value of an ARMA-GARCH Model

An ARMA(p,q) model is given by $ \qquad \qquad Y_t = c + \sum\limits_{i=1}^{p}\varphi_iY_{t-i}+\sum\limits_{i=1}^{q}\theta_i\varepsilon_{t-i} + \varepsilon$ with $\varepsilon_t \sim N(0,\sigma^2)$. ...
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Weekly Sales Prediction in R

I am new to prediction models, so I am trying different models to check which one is the best to predict the weekly sales in the next 2 year. However, I am not getting results from any of the methods ...
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What is Forecast Standard Deviation and is there a formula for it?

I'm familiar with statistics and errors for measurement such as Mean Absolute Percentage Error, MAE, MSE, MAD, RMSE, SMAPE (plus others) and their mathematical formulas but so far I've not found a ...
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Should “day forward-chaining nested cross-validation” actually be used to evaluate time series forecasts?

I'm very familiar with the standard test/train approach to forecasting, i.e. https://otexts.com/fpp2/accuracy.html This question concerns day forward-chaining nested cross-validation, see https://...
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Comparison of forecasting models at scale

I am working on a project and 225 time-series models were built. Their variations are related with the intervals in the data to be considered (1 year, 2 years or 9 years), the percentages of the ...
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What is the difference between sliding, rolling and expanding window in Time series forecasts?

What exactly is the difference between sliding, rolling and expanding windows in time series forecasting? Are rolling and expanding windows just subsets of sliding window? And which one of those ...
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How to emphasize newer data in time series cross-validation?

I'm working on a time series forecasting problem, and I'm looking to see if emphasizing newer data during cross-validation for parameter tuning increases performance. I started out using the scikit-...
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Including knowledge about structural breaks in forecast

I have a timeseries, where I know the volume will be about 20% lower in the future (because of a sudden policy change). I want my time series model (ETS) to pick up this change reliably, but I'm not ...
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How does AWS Forecast make probabilistic forecasts at a given quantile?

I would like to know which method is used by AWS Forecast to generate lower bound and upper bound time series forecasts at a given quantile? More generally, what is the method employed to make ...
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How to decide the shape of the input of an LSTM layer with temporally combined data

I'm working on a time series with 8 features, where at the beginning of each day, features 1 to 5 are unknown, features 6 to 8 ...
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In ARIMA models, given identical performance and same number of total parameters, is a pure AR model preferred over an ARMA model?

Say ARMA(5,0) and ARMA(3,2) provide the same (and best) cross-validation results. Is there any sense in which we can appeal to the principle of parsimony to argue in favour of ARMA(5,0)?
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Best way to forecast retention over a certain timespan

Let's say I have data, ...
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Forecasting with LSTM networks and use of final_hidden_states

I'm working on a seq2seq, stateless (return_state = False), forecasting problem. Let's say I have 10 independent time series with dimensions (10,50,2) where 10 is the number of samples, 50 is the ...
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Can we predict sales using time series or Regression

Input: I have a dataset that has sale from the last three years and does not have any missing information along with some other features like Date, Promotion, Store_number, Public_holiday_indicator, ...
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Forecasting monthly visitor count from daily values

I have a dataset of the daily visitor count of a website. Given this information, I want to forecast what the monthly visitor count will be. Depending on the visitor count on a day of the month, I ...
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Can someone help with ARIMA [duplicate]

Can someone help me with forecasting? My forecast doesn't predict well, I tried to use Fourier but I am new to this so, can someone explain how to determine a good forecast? accounting for ...
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Continuous transition between additive and multiplicative operator?

Question What is the best way to create a function/operator which can smoothly transition between addition and multiplication? More specifically, is there an alternative to just calculating the ...
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Can I use forecasted value to update neural network?

I'm trying to forecast the daily production of a well using a neural network trained on data from other wells, where the daily production of the previous day is one of the parameters. Since the ...
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ARIMA forecast (edited) [duplicate]

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How do I deal with nonexistant data in an irregular frequency time series?

I am trying to do some time series analysis on the margin resulting from three specific commodity futures contracts and ultimately forecast the margin. The margin is calculated as M = F1 + F2 - F3. I ...
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How do I interpret MRAE (Accuracy measure)?

Can somebody explain to me how I would interpret the result of the MRAE. In my textbook the MRAE is defined as followed: $$ MRAE= {1 \over n}\left(\sum_{t=1}^n \left|{e_t \over e_t^*}\right|\right) $$ ...
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Why use log predictive score?

I have seen a density forecasting paper using the log predictive score. There are many loss functions, but the authors suggest that the log score is local and proper. I don't understand why this makes ...
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how to fix the Volatility in the data and do forecasting in r?

I used auto.arima() to receive the p,d,q values,and checking the residuals. The model shows correlation and Volatility.The I used arch to check if there is the presence of ARCH effect, then yes, the ...
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Defining 'newxreg' argument correctly in the predict() function in R - ARIMA forecasting with tsoutliers [closed]

so I have a dataset Y which I have split into a training and test data set. I have used the tsoutliers function tso() to fit a model to my training data which consists of an AR(3) process plus 15 ...
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Machine Learning algorithms and Panel data

I have a large panel dataset composed of $N$ stocks, $T$ quarterly dates and $K$ features for each stock. The dataset looks like the following: ...
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1answer
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Forecasting in R without auto.arima()

I am trying to forecast data regarding vehicle registrations year-wise using auto.arima in R. However, one of my variables (which is data for 3-wheeler ...
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How to compute TBATS forecast accuracy without specific test set?

I have used the TBATS model on my data and when I apply the forecast() function, it automatically forecasts two years in the future. I haven't specified any training set or testing set, so how do I ...
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How to correctly calculate forecast KPIs without specific test set?

I have used the TBATS model on my data and when I apply the forecast() function, it automatically forecasts two years in the future. I haven't specified any training set or testing set, so how do I ...
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Predicting the dependent variable in R using an estimated model and predicted values of the independent variables with the forecast package

I am trying to predict future values of a dependent variable using predicted values of the independent variable(s) and an estimated model for the relationship between dependent and independent ...
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Modelling members who will switch to a new to product when it's launched

I have been asked to build a model that will help estimate how many insurance plan members will move from an existing product to the new product over time. The details of the project have yet to be ...
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ARIMA Cross Validation

I work with R and have got some questions regarding my ARIMA model. In specific, I have yearly data ranging from 1946 to 2019 and would like to do a basic two-step ahead ARIMA forecast for 2020 and ...
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Forecasting time series: bias and variance

Suppose I want to forecast $y_{t+h}$ with information available at time $t$ with many different models. The variance of the forecast error of model $m$: $e_{t+h|t}^{(m)} = y_{t+h} - \tilde{y}_{t+h|t}^{...
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Compare two forecasters on Brier score

I wish to compare two forecasters based on their historical performance (i.e. I want to determine who is better and by how much). The issue is that the two forecasters have performed a different ...

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