Questions tagged [forecasting]
The forecasting tag has no usage guidance.
126
questions
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9 views
Does the R hts-Package work with time series of different units?
currently I'm having a look at the wonderful hts-Package. Its idea is to forecast hierarchical time series (like described here). In this example the unit of measurement across all time series is the ...
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0answers
11 views
Transformer Decoding in Inference mode for Time Series
With the Transformer model from "Attention is all you need" you have to feed in the the actual target during training. However, this can obviously not be done for actual inference. Now usually for ...
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13 views
How to deal with a particular month being under forecasted by the model?
I have 6 cycles of historical time series data of retail sales and using a prophet model to forecast one cycle. I am using 4 variables as regressors. There is one month in particular - 'April', which ...
1
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1answer
44 views
How could I improve my FB Prophet forecast?
I've got 1325 days of revenue data and when plotting the components it makes 100% sense from a domain expert point of view, so the model is capturing the variations quite well (or it seems it does...)....
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3answers
46 views
What is the minimum requirement for the dataset for time series forecasting?
I have a dataset of patients where, for each patient, a measurement is taken 3 times per day. For example, patient 1 has recordings at 7.30 am, 12.30 pm and 8.30 pm. Patient 1 has a collection of 30 ...
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7 views
Huge forecast errors on certain days for an otherwise good ARIMA pricing prediction
I've built an ARIMA model for an electricity pricing forecast that gives a 24-hour prediction using 17 training days. The model automatically picks its parameters based on a minimal AIC score.
I've ...
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0answers
29 views
Time series forecasting produce same values with different training data
I'm developing a python program which predict daily timeseries values.
Each daily timeseries contains 288 values (a record every 5 minutes).
The main idea is to train a LSTM model with 7 days data ...
1
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0answers
20 views
Linear correlation and XGboost regression for time series
I am working with sales time-series data, I have a history of 9 years of monthly data. I am trying to forecast sales for the next 12 months. I am using XGboost regression to build multivariate time ...
4
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2answers
170 views
Time series forecasting dilemma. Could feature engineering overcome time dependency?
I keep reading articles about time series forecasting.
They all start from the same assumption: time series forecasting can't be treated as a regression/classification problem. It is time dependent, ...
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0answers
33 views
Does the add_regressor method on Facebook Prophet also work with categorical variables?
I went through the documentation of Facebook Prophet and was able to build a similar model for my time series dataset. The additional regressors I used were numeric. I achieved a reasonable MAPE score....
0
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1answer
24 views
Forecasting Consumption for Multiple Products for Multiple Regions
Came across a very interesting Real-World Time Series Forecast Problem.
Can you please help me understand the right track to resolve the below Time Series problem:
Input Data Sample:
and we want to ...
0
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2answers
20 views
Convey time lag information to a linear regression model
I am using a simple linear regression to predict the number of units an item has moved and price of the item is one of the input parameters.
For a few items, the older prices are not relevant and ...
4
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1answer
142 views
Forecasting via LSTM or XGBoost… is it really a forecast or
I guess I understand the idea of predictions made via LSTM or XGBoost models, but want to reach out to the community to confirm my thoughts.
This tutorial does a nice job explaining step by step of ...
0
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1answer
35 views
Forecasting time series outside the training/test set
I am trying to predict some time series based on precedent values using LSTM.
I have pretty good results when I compare the predicted time series with the test set (0,18% error)
I just miss how to ...
0
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0answers
8 views
What are good public datasets for time series analysis with “certified” (by papers in the literature) good predictors of the target variable?
I have to test different models for time series forecasting and predictors (exogenous covariates) goodness evaluation and I would like to use datasets used in relevant scientific publications that ...
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2answers
53 views
How to normalize a data set of multiple time series?
I have the a data set representing the electricity consumption of 25 000 customer.
The electricity readings are taken from each smart meter each 15 min for a period of 3 days. The data is takes from ...
0
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1answer
17 views
How to measure/rate the effect of a exogenous covariate in a ARIMAX Model?
I have an ARIMA model, I'm trying to figure out how much an external variable (exogenous covariate) could improve the forecast, so I need to "synthesize" a rate that tell me the usefulness (or impact) ...
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21 views
How to understand logistic trend parameters proposed by Prophet (Facebook)?
I read the paper and I saw that the logistic trend is defined as below :
$$ g(t) = \frac{C(t)}{1 + \exp{ -k(t) (t - m(t)) }} $$
Where $k$ and $m$ are respectively a growth rate and an offset parameter....
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21 views
LSTM forcasting: time series as input and a unique value for each as output
I am sorry for the mistakes I will make in this post, I'm new to deep learning ^^'
I am trying to build an LSTM model that can help me predict some unique value according to time series indices and ...
2
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0answers
35 views
Problem with Prophet Model regressior
I am working on predicting half-hourly UK electricity prices with prophet. I have two other time series: gas prices and initial national demand out-turn. So, after merging all the data-sets together ...
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0answers
9 views
Context Dataset in Time Series Forecasting
In time series forecasting, what is context dataset? How different is it from the actual training dataset and what role does it play either in training or validation?
1
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0answers
50 views
How to combine data having similar distribution?
I have a collection of time series data with data points of around 2 years of daily data. I am thinking of a way to increase the number of data points in it so that the neural network gets a better ...
0
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1answer
25 views
forecasting - likelihood of customers participating in next month sales
I have historical transaction information of customers for the last 2 years and other information about the customers like what type of card (gold/platinum) they used for transactions etc. is also ...
0
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1answer
28 views
How to find feature importance with multiple XGBoost models
My problem statement : Time Series forecasting(Month wise data), training on 96 months of data and predicting next 12 months with a 3 months empty window in between.
Example :
Batch 1
...
2
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0answers
26 views
Time Series and Statistical Learning methods differences
I am trying to understand the Time Series and Forecasting methods.
I have this basic theoretical question about this:
Why "Time Series" is NOT included on the Contents of, for example, the book "...
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0answers
8 views
Cross-validation for Timeseries Counterfactual Analysis
We are looking to predict counterfactual states from time-series data. In our problem we are looking to determine the energy savings from a grid-installed device that is varied on and off for many ...
1
vote
1answer
134 views
Forecasting multiple time series with a single model
I have a dataset with sales numbers for ~500 different markets (assume different cities or regions) and need monthly forecasts for each market. Instead of building 500 different models, I'm interested ...
2
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0answers
20 views
Why might an LSTM be capable of predicting an ARMA signal but not a linear combination of ARMA signals?
I have an LSTM network and am testing it on some dummy ARMA signals. I'm trying to predict the signal 5 time steps into the future.
The network is capable of outperforming Naive (persistence) when ...
0
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0answers
13 views
For time series forecasting task, should I use data across several time steps or singe timing data for prediction?
I have a time series forecasting project, there are over 10, 000 time steps of data, so the data amount is not a problem. At first, I thought I've to create a time-based data pipeline that forms the ...
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0answers
8 views
How is possible the result of GRU would other way around compared to LSTM while they were implemented samely?
Recently I crossed to a situation I can't figure it out why it happened. I applied six predictive models on a specific dataset as training-set and tried to predict the other similar dataset as an ...
0
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1answer
40 views
LSTM with input of actual time step
I'm working on an implementation of LSTM neural network to forecast energy consumption. I have a dataset with load, series of weather parameters and indicator of it's bank holiday or not.
I first ...
0
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1answer
89 views
How to make forecast in LSTM with specific/defined feature values
I am analyzing the avocado dataset to predict the future prices of avocado depending on the region and type (organic/conventional).
I've trained my model which seems to be working. The test results ...
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0answers
32 views
How to time series forecast with multiple time series data sets on the same time series index
How does time series work with multiple time series data sets on the same index?
For example, suppose I were a utilities company. Suppose I have the electricity usage of two homes, each indexed for ...
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0answers
20 views
How to get rid off differences between prediction and actuals while predicting or forecasting time series data
I have performed Boxcox transformation on my time series data and processed it through ARIMA modeling. Converted prediction values to the actual. I see significant differences between actual and ...
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0answers
9 views
What kind of feature engineering suits for random generation like time series data
My time series data looks like random generation which was obtained by aggregation day-wise sales of a product. The dependent variable does not show any kind of pattern.
So have tried tries different ...
0
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1answer
37 views
How to forecast product/item sales next one week using Xgboost regressor
I have trained xgboost algorithm to predict the number of items sale on a given day and got pretty good results, now I would like to forecast sales ahead of one week.
I tried re-training the ...
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0answers
37 views
Is it possible to use LSTM for time series forecasting for future months with test data having only NaN values?
my dataset is a univariate time series with one column as months, other column having demands for the corresponding months. My test dataset has NaN vals only for all the months. Can LSTM be used in ...
1
vote
1answer
64 views
How to split a dataset into train and test sets for time series (multiple step-multiple output forecasting)?
I am trying to use an LSTM neural net to do multiple step / multiple output forecasting (I predict multiple values in one time knowing some values in the past). But, I have realized that I must be ...
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0answers
10 views
How to get quantiles/probabilities of time series forecasts?
my problem is as follows :
I am creating demand forecasts for some goods with different methods (ARIMA, ETS,..) The issue is that I would like to forecast the probabilities of those forecasts since ...
0
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0answers
9 views
What's the meaning of Err in Online Time Series Predicion with Missing Values
I'm reading THIS paper about online predictions on time series with missing values. And trying to code the third algorithm in C++.
The thing is that I don’t understand what they mean by $Err_{\tau}...
2
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1answer
31 views
Building a time-series demand forecasting model
I am forecasting demand for certain types of goods and services, which I expect to be correlated to a sub-set of a basket of macroeconomic indicators (considering 15-20 indicators)
I do not know ...
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0answers
39 views
Training multiple multivariate time series
I have just learned LSTM for one month, and I am doing a project that aims to train an LSTM model forecasting the taxi demand at "t+1" according to the taxi demand at "t", "t-1"... In particular, I am ...
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0answers
19 views
How to Manipulate date data for multiple visits per person?
I have a query to solve. I have data regarding customers and number of visits done to them. These are in two tables. So I want to join two table and create different features so that I can find better/...
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0answers
35 views
Finding p values and estimates of external variables in facebook Prophet for forecasting in Python
I am using facebook Prophet for multivariate forecasting which has an objecting of forecasting prices. My target variable is affected by n number of external variables too.
I am using add_regressor ...
0
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0answers
15 views
A way to identify anomalous trends amongst several trends?
I'm working on a personal project, where I'd like to identify anomalous trends. Here's the scenario:
Imagine a company can sell 3 types of say, candies: X, Y, and Z. For some reason, these prices can ...
0
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0answers
20 views
ARCH and GARCH model working and R code for its implementation
my time series data set doesn't have a proper trend or seasonality. Heard about ARCH and GARCH model which works fine on Un-trended data set but I am not finding the proper R code for its ...
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2answers
95 views
How to predict next visit date based on this data
I have a dataset shown below. Here, status is if visit has been done or not and schedule is if next_action_scheduled.
...
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0answers
84 views
Generalization of RNN/LSTM/GRU… model
Given a time-series prediction with a Recurrent Neural Network (doesn't matter if LSTM/GRU/...), a forecast might look like this:
to_predict (orange) was fed to the model, predicted (purple) is the ...
1
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2answers
209 views
What arguments should I pass to input_shape parameter of LSTM function in Keras?
My dataset has 2944424 rows and 6 columns. I am using an LSTM in Keras to forecast taxi demand. I am having problem with the input_shape parameter of the LSTM.
It ...
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0answers
21 views
What approach should I take to model forecasting problem in machine learning?
I have a dataset which contains 4000k rows and 6 columns. The goal is to predict travel time demand of a taxi. I have read many articles regarding how to approach the problem. So, every writer tell ...
