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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Multistep horizon strategies vs Seq2Seq

In the context of recurrent neural networks for forecasting, what is the relation between multi-step horizon strategies and RNN-based seq2seq? For example, is the following Multiple Output Strategy [1,...
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How does one perform a Canova-Hansen test in Python?

I am referring to the documentation here, but it does not give many examples on how to actually perform the test. I have a pandas dataframe with two columns: Column 1 is first day of every week, ...
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predictions based on irregular repeated measures?

I need to make a model that predicts certain medical outcomes based on the answer to health-related questionnaires. Providers have patients fill out these questionnaires more than once, at irregular ...
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Why is Orange (CSV import or Line Series) doing some weird rounding on my data?

The data I have is in tab-separated format (exported from MetaTrader5): ...
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Detrend a time series

I am fairly new to forecasting and I am trying to create a demand forecast for my organization; I am following the methodology outlined here. In step 12 of the process, the author subtracts the trend ...
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Use two different but correlated time-series signals as two different samples to train a model

I want to train a forecasting model for signal A initially. In the future, forecasts B and C may be required. These are all financial time-series signals with the same resolution. The issue I am ...
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Forecasting physical performance of young football players

I have a dataset containing physical testing data from a football academy, including sprint tests, COD test, endurance tests, strengths tests, and anthropometric features. The dataset contains all ...
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Why is my LSTM prediction is saturated and have bad prediction?

I am new to deep learning. Currently, I am trying to predict torque based on its past values using an LSTM model. There are two datasets (generated from a scaled test), one with wear and the second ...
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is there a standard metric to check volatility between every week forecasting results?

I have fitted arima, tbats model on my dataset and forecasted results for 12weeks. my forecasting results from 1st week to 2nd week differs a lot. I checked how much is the variance(volatility) ...
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Time Series Forecasting in Python Question

I am trying to understand what is the best possible way to execute a time series forecast. I am trying to forecast the number of employees that are going to call in sick on a given day. My data has ...
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Time Series Forecast: how to deal with NA-values in lagged predictors of the target variable?

For my time series forecast of several consumer products, I am using lags from the target variable. In order to forecast 24 month, I am using 24 lags (24 tend to be a bit more accurate than 12, but ...
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Which time series model to choose?

I am new to time series forecasting. I have two very large dataset consisting of about 530k values obtained from a scaled dataset. The nature of both of these datasets are different. One of them has ...
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Sktime - how to make in-sample and out-of-sample forecasts with exogenous variables?

I'm trying to make forecasts using sktime for my entire training data and an arbitrary length of out-of-sample data but can't figure it out. ...
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How do I prepare time-series data to predict the change in a response variable, rather than the value of the response variable itself?

I am forecasting solar irradiance using different time series models. Rather than predict the irradiance from $t=0 \text{ min}$ to $t=120 \text{ min}$, I would like to predict the change in irradiance ...
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ARIMA predictions look shifted by one unit of time

I am using statsmodels ARIMA (1,2,1) to predict the monthly demand for a product. The predictions look like they are shifted to the right by one month. I wonder if the statsmodels.ARIMA.Residuals....
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Demand Forecasting/Regression task for new products

I'm currently at the end of my master's degree and have to solve a data science problem. I am currently kind of stuck and need some kind of advice to get better results. I want to share the task I ...
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Walk-forward validation nrounds LightGBM

Dear Data Science Gurus, I am facing the following phenomenon: 1-Scenario: Predict demand for n-month in the future for hundreds of products using lightgbm. I have 2 years of history 2- Approach: Walk-...
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Time series forecasting model predicts increasing number for target variable when the actual values are zeroes

I am working on time series forecasting model, and I am using light gbm. The project goal is to predict the number of sales across different levels (very similar to the M5 competition). For instance, ...
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Can anyone help me with this error. I did the following code but it does not work and I am getting the following error

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Anomaly prediction/forecasting in timeseries?

What options exist in order to forecast when next observation will be an outlier in a time series? Initially, I thought to train a simple forecasting model, which turned out to decently predict the ...
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Connecting timeseries quantities to CDF

In the following paper, ...
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What model to use to forecast on small dataset with many parameters?

I have a dataset of 20 columns/parameters (x's) and not many rows (10-20 historical values for each x) I need to use to predict a y column for the future 5 years. Each row represents one year. What ...
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predict next purchase time of an item

I have a bunch of timestamps (purchase date from history), that looks like: [1658753101, 1658760061, 1658824861, 1658846461, 1658853961, etc] What I want is to based on that list predict next item ...
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Univariate Time Series Revenue Forecast Flow for Multiple Products (Different Products in Same Domain)

My task is revenue forecast. I would like predict 7 days horizon for each 10 products. I am planning to use ensemble model. Can 10 products(same domain, for ex: ios game app revenues) be predicted ...
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Multivariate timeseries classification for each group in a dataset

Let's say, I have the following dataset: ...
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How to obtain data for warehouse throughput forecasting project?

I am trying to build a machine learning model to predict warehouse throughput. I do not have any domain information or data since I am supposed to build a generic prototype for my clients. I am going ...
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Usage of SVR in Non Stationary Time Series Forecasting

Based on my knowledge of Support Vector Regression(SVR), One of the assumptions of SVR are Independent Identical Distribution. So the application of SVR in forecasting of non-stationary Time series ...
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Predicting children growth

I am doing a project where I am supposed to forecast future athletes' performance one, two, three, etc. years in the future. The dataset consists of athletes' scores on tests done from they were kids ...
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Model prohet : multi variate?

I would like to have your opinion on the use of the prophet model as a multi variate model. From my research it can be used with an 'add_regressor' but in my experience the performance degrades when I ...
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Temporal rows selection for Recurrent Neural Networks

I have a time serie $x_{1},...,x_{n}$ with a temporal step $\Delta = date(x_{i+1}) - date(x_{i}) = (i+1) - (i)= 1 \ day $. For each $i \in [\![ 1,n ]\!] $, I know that the value of $x_{i}$ depends ...
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Conceptual question on time series metrics

Consider a multi step time series forecasting problem. The input to the model is a time series (say the temperature every hour for 12 hours) and the output is also a time series (the predicted ...
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Train a unique model over multiple time series

I'm currently working in a project involving time series. I have nearly 100 univariate time series (representing the performance of an engine of cars between 2018 and 2022). My goal is to forecast the ...
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How should I assess when to retrain my model?

I have trained a model to generate a rolling 12-month forecast (1 prediction each for the following 12 months) using deep neural network architecture. The model is trained with almost all the data ...
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How does global forecasting work in comparison to local/univariate forecasts in terms of generating predictions?

Recently there is a lot of discussion about global forecasting models and their advantages/disatvantages compared to local models. My question is not about this comparison. I want to know the ...
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Choosing a model to forecast parallel time series with multiple features

I have 6 websites, and I am trying to forecast the number of chat bots opened per hour for each website. The time forecast is 72 hours later. Data Format There are 15,000 data points (deseasonalised),...
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How should a dataset looks like for Time series forecasting

What should a dataset look like for time series forecasting? Can I do time series forecasting with a dataset that contains apartments from ad sites obtained with: web scraping from 2018 to 2021 13 ...
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Strong bias from Linear SVR meta model

I have built nine meta models based on the model stacking principle, which I compare to a reference model for a number of time series. See the results below. The 22 base models that are trained on 70% ...
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Why would a Linear SVR model greatly outperform a Linear Regression model on model stacking

I have built nine meta models based on the model stacking principle, which I compare to a reference model for a number of time series. See the results below. The 22 base models that are trained on 70% ...
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Unable to remove Seasonality

I have sales data which is seasonal and has no trend. The frequency of this series is 15 mins. I don't know how to compute the exact period of seasonality - whether it is daily or weekly or monthly or ...
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Temporal Fusion Transformer from PyTorch-Forecasting with Multiple Targets - 'list' error

New to PyTorch and the PyTorch Forecasting library and trying to predict multiple targets using the Temporal Fusion Transformer model. I have 7 targets in a list as my targets variable. I'm using ...
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How to make XGBOOST capture trend in time series forecasting?

I am trying to forecast some sales data with monthly values, I have been trying some classical models as well ML models like XGBOOST. My data with a feature set looks like this with a length of 110 ...
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One-sided time series trend-seasonal decomposition

TL;DR: Are there one-sided decomposition alternatives to the naive seasonal_decompose from statsmodels? Are there approaches to adapt intrinsically two-sided ...
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Wave Function in Python

How to apply Wave Function to a Data Set in Python to derive frequency distribution and probability amplitude?
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Make fitted xgboost or linear regression model predicts values in thé future

I have a machine learning model that I fitted with xgboost and linear regression. My dataset has thirteen features and has price as the target. Is there any way to ...
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Forecasting future point with partial future data already available

Working on a forecast model that should output an End of monthly value, the interesting part is that we already have partial (90%) of that data available at the prediction point (max 30 days away). ...
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Which machine learning technique can be used for predictive log analysis

I have log data with 100k records. And These parameters. It looks like this. message types can be helpful for anomaly type detection. Out of total 15 message 5 ...
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Forecasting the price of an electricity stock each hour for 3 days with the help of weather data?

I'm new to forecasting and I want to know where can I find information that help me in choosing an appropriate model for this need. What is your opinion on this given that the model needs to predict ...
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Is there a way to forecast a time series multiple linear regression using externally made dummy variables?

This question concerns question 4h of this textbook exercise. It asks to make future predictions based on a chosen TSLM model which involves an endogenously (if i'm using this right) made dummy ...
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Discrete wavelet transform - DWT (beginner)

I recently stumbled upon this article : https://www.bportugal.pt/sites/default/files/anexos/papers/wp201612_0.pdf In the paper they use DWT and I am having trouble understanding how to construct them. ...
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Removing seasonality in time series forecasting

In time series forecasting we are removing the "seasonal" component to fit models better and have better forecasting. But why? if I should give an extreme example: if I have a sin wave, I ...

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