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Questions tagged [forecasting]

Forecasting is the process predicting future values based on historic and current data, typically for time-series datasets.

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Does nowcasting use cross sectional data?

So in recent months I have been reading about nowcasting. From what I understand what UMIDAS does is that it transforms the dataset into cross sectional data and then runs OLS. The more I read ...
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Univariate time series forecasting with bimodal distribution

This is my first ML project ever. Well the objective is to build a forecasting model of a univariate time series containing solid waste weights loaded from the city of Austin,Texas. The distribution ...
karim abousselham's user avatar
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Misaligned Multivariate Time Series (weather and soil moisture) in soil moisture forecasting

I'm working on soil moisture forecasting using multivariate time series (MTS). More precisely, each time sample comes with multiple measurements regarding: the soil (e.g. soil temperature, and soil ...
Marco DC's user avatar
3 votes
2 answers
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How bootstrapping works for prediction intervals?

I'm experimenting with prediction interval (PI) over univariant time-data using skforecast pythonic package.. in the documentation it is mentioned that: Prediction intervals A prediction interval ...
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Time Series forecasting with SVR

I am trying to forecast my data by Support Vector Regressor, Here is my code: ...
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How to correctly use gstar package of R for spatio-tempral analysis?

I want to perform a spatio-temporal analysis by highlighting spatial as well as temporal dependencies of the data (I have a 'weight matrix' highlighting spatial dependencies of the counties) on the ...
Shashank Gupta's user avatar
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Time Series forecasting feature creation/engineering

I'm new to time-series-forecasting and was wondering, whether in a single variable forecast e.g.: X -> Y the creation of additional features of X leads to an improvement when training. So if adding ...
user159972's user avatar
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Is there a way to check if a time-series is seasonal without manual inspection of its decomposition?

While using various models to generate a forecast of a time-series it is needed to know if a time-series is seasonal or not. I have searched about this topic but could only find people mentioning ...
Mathias Hillmann's user avatar
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Calculate the best alpha, beta and gamma that should be used in the holt-winters exponential smoothing formula

With the following code I can find the best forecast value for a series by iterating over the alpha, beta and gamma and saving the result with the best RMSE: ...
Mathias Hillmann's user avatar
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How to add lagging Features for Forecasting with a random lag range without adding a new column per lag?

The common way of adding lagging features in time series forecasting problems is adding lag columns with pandas.shift(). While it is a fine method but what about when wanting to use a random integer (...
Emad Ezzeldin's user avatar
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Gradient boosting to forecast just one-step ahead

I'm training a gradient boost algorithm (trying both XGBoost and LightGBM) for cash flow forecasting. I was able to do it well separating my training and test sets using the default separation (80/20) ...
vibebizarrinha's user avatar
2 votes
1 answer
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Should I remove or interpolate missing values?

I have a dataset containing a very long time series of hourly traffic congestion in a certain city, during a period of ~22 years (number of data points: Roughly 24 X 365 X 22 = 192720). I want to use ...
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How to predict multi-variate time-series from different samples [closed]

I'm having issues seeing the best way to predict a time-series when training on a dataset with different samples. I have a dataset that shows the weight of 10 rabbits from their first day to their ...
scootjow's user avatar
1 vote
1 answer
81 views

When is it necessary to remove seasonality from multivariate time series?

I have a complex time series dataset that I'm exploring (https://archive.ics.uci.edu/dataset/501/beijing+multi+site+air+quality+data) and I've detected some regular hourly seasonality in the data (not ...
Joshua Noble's user avatar
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119 views

How to detect and predict sensor faults and failures (for weather stations to be specific)?

Need help. Especially those knowledgeable in weather systems/meteorology. Best approach in detecting and predicting faulty weather sensors and their failures based on their readings alone? I'm doing a ...
noob101's user avatar
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Forecast proportions through time

I want to predict/forecast the proportion of positives (sample testing positive) over a 1-3-6 month period. My data has a lot of negative sample tests, therefore, it's aggregated by month. Let's ...
Alejandro L's user avatar
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Correlation in forecast error

I am working on a time series project and while calculating the prediction intervals using bootstrapping method I have to fulfill the assumptions of it. It has an assumption of uncorrelated forecast ...
Aakash Goyal's user avatar
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Demand Forecasting methods for hundreds of time series

I have TFL cycling dataset for the time period from JAN 2023 to JUNE 2023. I would like to forecast the demand or expected no. of trips for each station at every hour of the day. Post some data ...
a_jelly_fish's user avatar
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Lack of Variability in Predictions from Multivariate LSTM Model

I've been working on a multivariate LSTM model for time series forecasting, but I'm encountering an issue where the predicted output doesn't exhibit enough variability. The predictions tend to be too ...
Pavol Krajkovič's user avatar
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Is it possible to use leftovers data (warehouse stocks data) to create sales forecasting?

For example, i have sales data by categories | Date | GE | VIC | | -- | -- | --| |03.01.2022 |2|7| |10.01.2022 |30 |12| |17.01.2022 |15 |5| |24.01.2022 |57 |8| |.....|...|...| |28.08.2023 |16 |2| And ...
Holo's user avatar
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Winter Holt's Time series model

I am confused with the Winter Holt's Time series model usage. I use 2 years of data to train and want to predict 3rd-year data. Note1: I have partial 3rd year's data, but I want to use it to check my ...
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Can lag features be applied into test data without label?

can lag features be applied into test data without label? I've been wondering. I tried to build random forest model using dataset: training data (with label Y) and testing data (without label Y). The ...
thenoirlatte's user avatar
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Stable test in online time series forecasting problem

I have a Time Series Forecasting problem. You can think of it as predicting the daily closing prices of Apple stocks. My data is divided into 4-day segments, and the forecasting is based on predicting ...
Angerato's user avatar
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How do we modify the early stopping procedure to account for better losses after initial rise in losses?

I have a question regarding the usage of early stopping in the training of my forecasting model. Curious about how the training would go without early stopping, I observed that the test loss seems to ...
Zezimabig's user avatar
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Can a multivariate MIMO LSTM forecaster learn the relationships between the multiple feature outputs?

Question: Can a multivariate MIMO LSTM learn the relationships between the multiple feature outputs? This question arose when I decided to modify a multivariate (Multiple Input - Single Output, MISO) ...
Zezimabig's user avatar
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Predicting quanting sold using Time series data

I am struggling with a time series dataset comprising 12 features, including quantity sold and weather data, totaling approximately 1800 values. My goal has been to forecast future values, quantity ...
BasicTex's user avatar
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35 views

Calculate the curve of a list and apply that to calculate the values of a larger list

I have a list of ~125 sorted ascending values and I am converting those to percentages of total so that they add up to 100% (shortened example would be: 0%, 0%, 15%, 15%, 30%, 40% with the total of ...
Disz's user avatar
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1 answer
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How to assign sample weight for regression problem

I'm trying to model a forecasting problem where I'm trying to forecast for the following month. I am using LightGBM Regressor class for it and it giving me a decent ...
Krishnang K Dalal's user avatar
1 vote
1 answer
79 views

Is timeseries forecasting for the next timeslot with a RNN a "Many-To-One" type application?

you often find applications that divide RNN according to their input and output data into the categories: One-To-One One-To-Many Many-To-One Many-To-Many as you can see e.g. (here https://...
PeterBe's user avatar
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Forecasting using LSTM Model

I have a dataset with 12 variables (x1, x2, ..., x12) and one target variable (y). Is it possible to perform a forecasting for the target variable (y) over a certain period of time ahead without ...
Kayla's user avatar
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Prediction intervals for future timestamps - out-of-sample

I've created a model for out-of-sample forecasting that uses multistep recursive strategy to reduce my problem to regression, the predictions are sufficient but I was wondering if there is any ...
kkkk0's user avatar
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Good candidate time series models for 70-100 monthly data points, also incorporate past and future exogenous covariates

Per title, we're trying to identify a good time series modeling technique for: 70-100 variables of monthly sales or volume data (2015 or 2018 to present) Ability to forecast not only using trend and ...
Vaslo's user avatar
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1 answer
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Forecasting sequence data with intermittent peaks

I'm trying to forecast a sequence that looks like below: I know ARIMA, INAR, GLM, etc. but none of these works for this data. Algorithms I found for intermittent time series (ADIDA, Croston, etc.) ...
Haochen Wang's user avatar
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25 views

Growth (sales) forecast based on season

How to make a forecast based on last year (season)? I have no experience in the field Make a forecast of the sales in september 2023 based on last years sales Example A Sales august 2022 (last year): ...
clarkk's user avatar
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How to predict in a forecasting model when the data after training and prediction is missing?

Let's say we have a forecasting model that was trained on any data before 2021 and now we need to make a prediction on data in 2023, for an accurate prediction we need to either give the data of 2022 ...
Sadaf Shafi's user avatar
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145 views

how to increase the accuracy of Prophet model?

I have a dataset representing the number of tickets over two years. I have used the prophrt model to predict make a prediction but the results don't seem to be accurate. The code for the model is as ...
noobie159's user avatar
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Secondary model to correct RNN-based photovoltaic power generation forecast: Useful and what model type?

I am currently working on forecasting short horizon photovoltaic (PV) power generation. My primary forecasting model is an RNN which aims to forecast the PV power generation from the current day until ...
BragorLL's user avatar
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23 views

Forecasting with exogenous variables

I have the following time-series data: ...
Louis GRIMALDI's user avatar
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148 views

Why does Time series split cause data leakage from future data?

There are lots of websites saying time series split may cause data leakage. The idea for time series splits is to divide the training set into two folds at each iteration on condition that the ...
Ellen's user avatar
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LSTM multivariate forecasting

I'm currently working on timeseries forecasting in pairs where one timeseries is suspected to cause the other timeseries. I also have the forecast of the causing timeseries and I use them to predict ...
Noam_I's user avatar
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Inverting the scaling of LSTM forecasts (tanh activation function)

I am working in R and the monthly time series of the M3 competition data (with values mostly around the 1000s). I want to scale the series from -1 to 1 in order to satisfy the tanh activation function ...
Duy Nguyen's user avatar
-1 votes
1 answer
26 views

urgent!! i want to forecast health expenditure but the data is very small, only have a dataset of 20 samples

i tried using ARIMA but cannot due to small dataset. anybody know other time series forecasting for small dataset of 20 sample? urgent need help
Basil Zikry's user avatar
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1 answer
50 views

Can I train an LSTM on two features to predict a third?

I have a multivariate time series with three features x1,x2,x3, I have chosen window size 5 to make a prediction on variable x3 at time t=6. Is it correct to use only the LSTM input on the variables ...
Stefano Morrone's user avatar
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Trying to capture global time dependences for prediction with few time steps

I am trying to figure out the best approach for my prediction task. I have a dataset with four variables: year ranging from 2010 to 2022, categorical variables $A$ and $B$, and numeric target variable ...
Merry's user avatar
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How to deal with systemic gaps in timeseries data

To be clear this question is not how to input missing data, but how to treat an exchange dataset that will not ever have data on weekends and occasionally on market holidays. I'm working with the ...
steezJobs's user avatar
1 vote
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what is the error here ? time series sliding windows

Given a list where each element is a dataframe, i want to create sliding windows in order to train a lstm model, but the problem is an error occurs. Each dataframe is a time series with the 4 columns ...
heyoka955's user avatar
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21 views

How to re-translate a forecast I wrote in R run on a dataset I downloaded from Tableau back into a SCRIPT_REAL function in Tableau

I want to add a SARIMA forecast of the next two days onto each line in the following Tableau graph: But Tableau only does Exponential Smoothing forecasts and if I create a moving average table ...
Marlen's user avatar
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Growth trend in time series

I'm currently working with a dataset that looks as follows: I'm using the prophet model provided by meta to forecast the data and I'm quite unsure about the parameter called 'growth'. Now there are $...
Louie's user avatar
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424 views

What is the difference between lookback period and transform a time series dataset into a supervised learning dataset for time-series forecasting?

Let's say I have dataset within the following pandas dataframe format with a non-standard timestamp column without datetime format as follows: ...
Mario's user avatar
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1 answer
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What is the best\correct data split approach over time-series data to compare performance of forecasting future data among ML and DL regressors?

Let's say I have dataset contains a timestamp (non-standard timestamp column without datetime format) as a single feature and count as Label/target to predict ...
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