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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Forecasting mon missing time timeseries data

I have time series data for minutes interval. But due to some noise i have to remove some rows from data. Now, I have data with some missing time stamp. What should i do for forecasting in this case?
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In Time Series forecast, should Scaling be done on both train and test features combined ( test is 1 new data point)?

Let say I have a Time series, I'm using sliding/expanding window method to split to train and test data: train would be all the data I have until day x and test is day x+1. To avoid Data leakage I'm ...
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18 views

Machine Learning Model for Time Series Forecasting

I am using Random Forest, SVM, and XGBoost models to nowcast/forecast an economic time series variable. However, I would like to extend these models to optimize/customize them for time series ...
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21 views

One Year Ahead Forecasting with Unevenly Spaced Time Series

I have many products in my warehouses which can be "demanded" any day by my different clients. I want to forecast how many of each item will be demanded for the whole next year. Naturally, ...
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Time series forecasting for stable pattern with some sudden changes

In my case, the time series is around a constant value with very small fluctuations. But, sometimes the signal starts increasing or decreasing for some duration of time. For my application, such ...
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Time series forecasting when one of the series is known

I have a problem where there are two time series $\{x_t\}_{t \geq 1}$ and $\{z_t\}_{t \geq 1}$. These two time series are correlated for fixed time instant but uncorrelated with each other across time....
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23 views

Predicting out-of-sample time points with LSTM

I'm working on a time series forecasting problem using LSTM. The data is univariate and non-stationary. I followed the this tutorial. The data is processed as the following: First, the difference ...
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29 views

Forecasting with Neural network and understanding which underlying model is favoured

If I have a very large set of data (~ 1TB). How can I use Neural Network on this data to understand which underlying distribution (eg. let's say a Gaussian or a Poissonian with a certain mean, sd) is ...
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Multivariate time series forecasting with the extended Theta method

I am looking for an implementation (better if in Python) of the extended Theta method for multivariate time series forecasting, presented in the following paper: D. Thomakos, K. Nikolopoulos, ...
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33 views

Is time series forecasting possible with a transformer?

For my bachelor project I've been tasked with making a transformer that can forecast time series data, specifically powergrid data. I need to take a univariate time series of length N, that can then ...
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Neural net performance using rmse

I am trying to build a NN which can predict exchange values. I am quite new to R and NN and I don't quite understand how I could improve the performance metrics of the neural network. I have tried ...
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22 views

Predicting high frequency sparse time series data in python

I have a dataset of a couple of EV charging stations (10 min frequency) over 1 year. This data consists of lots of 0's, since there is no continuous flow of cars coming to charge but rather ...
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Predicting sparse time series data

I have a dataset of a couple of EV charging stations (10 min frequency) over 1 year. This data consists of lots of 0's, since there is no continuous flow of cars coming to charge but rather ...
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19 views

Embedding a categorical variable and concatenating with a numerical variable, in a many-to-one sequence problem with multiple features

I have a small data set where I track 4 variables across 4 time periods, 1 categorical and 1 numerical variable. Below is picture of data set that I am using: cat1 - Categorical variable encoded num1 ...
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Azure ML / AutoML: problem with univariate time series forecasting

I'm having troubles generating univariate time series forecasts with Azure Automated Machine Learning (I know...). What I'm doing So I have about 5 years worth of monthly observations in a dataframe ...
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How can I intuitively calculate the accuracy of my financial prediction model?

I've built a SARIMAX model based on my personal spendings record as a college thesis and have reached a point where I'm pretty content with how it turned out and am getting ready to start writing the ...
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Supply Chain forecast with Covid-Data

I am working for a large food retail company and we are using ML models to predict the demand of certain products for the weeks to come. Of course, looking at the sales distribution, 2020 was a ...
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Elman RNN with keras

I have to perform multi-step multivariate forecasting of time series, using keras. I found an example where LSTM is used. I could modify that example replacing LSTM with SimpleRNN. Now I would like to ...
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Demand Prediction (Retail Sales) Datasets for Benchmarking

I've been looking for 2 days and can't seem to find what would normally appear as trivial: a Time series (preferabl daily or intra-day resolution) Demand or Sale Labelling Retail Dataset Preferably ...
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203 views

Problems to understand how to create the input data for time series forecasting with a recurrent neural network in Keras

I just started to use recurrent neural networks (RNN) with Keras for time-series forecasting and I found this tutorial Forecasting with RNN. I have difficulties understanding how to build the training ...
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Using forecasting values of wind speed at different hours in the future to predict power output at different hours in the future for a wind turbine

I need to design a neural network model for a wind turbine which takes as input the forcasting values of wind speed as follows : windspeedPlus001hr , windspeedPlus002hr , windspeedPlus003hr , ...
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23 views

How can i find the accurcy for time series? [closed]

I have to work for the first time with time series and I have some question about this interesting field of machine learning. What I have to do: I should make a forecast for quantity for some special ...
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Multidimensional Output from Radar Imagery and Climate Data

I am trying to predict what my rainfall field will look like at a future timestep using: Radar imagery of rainfall fields at previous timesteps: A set of 2D matrices where each element in each matrix ...
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38 views

Time series forecasting. How use future values

I have a time series dataset containing hourly data from a few year, like below. Let's assume that I want to make prediction for the next 3 hours (2021-01-01 19:00, 2021-01-01 20:00, 2021-01-01 21:00)....
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39 views

Help improving time series prediction with LSTM on PyTorch

So, I am trying to use a LSTM model to forecast temperature data on PyTorch. I am relatively new to both PyTorch and the use of recurrent networks so I took a model I found on the internet to start. ...
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One LSTM for two currencies or two LSTM one for each currency?

Suppose I am building an LSTM model for currency forecasting. Assume that I am working on two rates: USD vs GBP and USD vs EUR. Should I build one LSTM model with input size of two features (GBP and ...
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Forecasting as a supervised regression task, with uneven length timeseries - how to split?

Consider a number of timeseries. Here we have 3, just to make it dead simple. Note that they're all different lengths. The very first thing I do is splitting by the original sample dimension: Then, ...
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18 views

How to determine my pacf and acf in ARIMA with daily time series data?

I have daily time series data for revenue sales ranging from 2018-04-01 to 2021-02-19. I want to implement an ARIMA model and the plot of the data looks like this: Based on my augmented-dickey fuller ...
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23 views

Demand forecasting with marketing budget data

I'm trying to build a demand forecasting model to predict future daily orders of an online food takeout service (similar to UberEats or DoorDash). My first model uses a univariate approach, which is ...
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129 views

Train MLP Neural Network on time series data?

Newbie question here but I was curious to ask if an MLP Neural type network can be trained on time series data? The dataset that I have is an electricity type data set from a building power meter and ...
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21 views

Can multiple time series take advantage of each other?

I want to forecast house prices market in multiple cities of the same country. I expect demography, interest rate and neighbors cities values to have an impact on my prediction. For every city, I have ...
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18 views

How to predict churn events that may happen within a period of time?

I am trying to build a model that predicts churn events in the future. The business wants to be able to identify which customers are likely to terminate the services within a month. "Within a ...
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useful method for analyzing the real-time (online learning) time series? forecasting and decomposition?

Which method is useful for analyzing the real-time time series? even for forecasting or decomposition? I have a large dataset (sequential data) that need for predication analysis and decomposition ...
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34 views

Data Cleaning Techniques - What to drop?

I have been struggling with this problem for a few weeks but it seems to be out of my league. I have a dataset of product sales over the course of 55 weeks. It contains information on the store ID, ...
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How to detect time for the future events in time series data?

I am dealing with IOT data from a mechanical machine. On the input I have ~100 features that are measured every minute. On the output, I have labels of zeros and ones, where zero indicates the absence ...
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Time series forecasting with constraints

I want to predict the passenger flow volume of an airline route, which subjects to supply capacity constraints of the route (i.e., the passenger flow volume should not be higher than the supply ...
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How far into the future can I forecast a time-series with an LSTM and strongly seasonal data

I am working on a Sequence-to-Sequence + Attention model for some time-series data. Now I have a really long time series, basically 40 years of daily observations for multiple sensors. The data itself ...
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8 views

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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30 views

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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32 views

How to account for rare events at different time intervals while using LSTM neural networks?

I'm working on an interesting sequence-to-sequence (regression) time series problem where some static features/rare events can change the behavior of future time series. The problem is a forecasting ...
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437 views

Difference between sequence length and batch size in time series forecasting

I am using Keras for time series forecasting and I am trying to understand the tutorial on the offical site of keras about time series forecasting that you can find here (https://keras.io/examples/...
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149 views

Azure automl time series forecasting error

I'm using Microsoft Azure automl to try and generate models for time series forecasting but I keep getting an error: ...
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Forecasting: Regression - Worse performance for a subset of data

I have a dataset for which I'm using different forecasting methods (Ridge/Lasso Regression / Random Forests / AdaBoost / Gradient Boosting) to compare their performance. For the full dataset, I ...
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Demand Forecasting - Error in predictions

I've been trying to do daily demand forecasting using the H2O AutoML package for using 4 years of daily sales data as the training set. However, the daily estimates always seems to find a range around ...
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Forecasting: Multiple Linear Regression (OLS) outperforms Random Forests / Gradient Boosting / AdaBoost

I'm using different forecasting methods on a dataset to try and compare the accuracy of these methods. For some reason, multiple linear regression (OLS) is outperforming RF, GB and AdaBoost when ...
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1answer
25 views

Long range forecasting with sequence-to-sequence models

I have a task where I want to forecast daily observations for 1 year or 2 years in advance at multiple locations--so 365 or 730 days in advance. I actually have a pretty good dataset, meaning daily ...
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23 views

LSTM model bad forecasts

I tried to implement LSTM model for time-series prediction. Below is my trial code. This code runs without error. ...
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Forecasting with two separate periods of time

I have a sales forecasting problem where I only have three months of year 2018 ( Jan, fev, march) and three months of year 2019 (oct, nov,dec) and I have to forecast the sales for all the months of ...

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