Questions tagged [forecasting]
The forecasting tag has no usage guidance.
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Is there an R tutorial of using LSTM for multivariate time series forecasting?
There is a great blog post about how to use keras stateful LSTM in R to forecast sunspots. I applied it to financial ts data ...
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0answers
6 views
Long run time for grid search SARIMA
I am running a grid search for identifying the right set of params for Seasonal ARIMA, for over a 1300 training set and range for all the params being 0,1 and 2.
But this process is taking over ...
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1answer
17 views
ML technique to predict next year output based on text quantities [closed]
I have a random data that I would like to predict how much a quantity will be in 2020.
The data looks like this:
...
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0answers
28 views
How to calculate customer purchase interval and predict next purchase in python?
Suppose we have a data set consists of columns
TransactionId, CardNo, TransactionDate
then how can we calculate the customer purchase interval (means if customer A purchased on Jan 1st and after ...
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34 views
Curve fitting with R
I was wondering if there is a package in R for fitting a non-symmetrical bell-shape curve?
3
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1answer
58 views
Is an Arma model equivalent to a 1-layer Recurrent Neural Network without activation function?
Given a time series $f(t)$ to forecast, let us consider an Arma model of the form:
$$
f(t) = c + \sum_{i=1}^p a_i f(t-i) + e(t) + \sum_{j=1}^q b_j e(t-j)
$$
where $e(t)$ are the forecast errors.
On ...
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21 views
Monthly trend with fb prophet
I have monthly data with month/year in one column and price on another. I would like to get a yearly trend with fb prophet library in python (how to use monthly data with the library is explained at ...
2
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1answer
42 views
Forecasting energy consumption with no historical data when there is a trend
I want to forecast new customers' energy consumption. Let's say I can construct a set of attributes to describe new and existing customers (e.g. size of business, type of business etc.) and I have the ...
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0answers
112 views
Understanding how to use ConvLSTM for multistep ahead forecasting
I have a problem where I have transaction data for many banking accounts. The task is to train a model on historical debit/expense transactions and then forecast expense transactions for the next n ...
2
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0answers
71 views
Recommended model for univariate or multivariate multistep ahead time series forecasting
I have a dataset consisting of recurring and non-recurring expense transactions from bank accounts, as well as other features describing the bank account and each transation. I aggregate these ...
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0answers
24 views
What is the correct format of the test input for LSTM neural network? [closed]
I have learned some tutorials for LSTM time series prediction. According to that tutorials, My training data format would be like,
NOTE :
This data is only for example. this is not the real data.
<...
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7 views
Stationarize count based time series data
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I have a count based time series sequence with lot of 0s.
Usually to achieve stationarity we can do the following transform:
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When is a weather forecast 'in-sample'?
I've got some weather forecast data and I want to split it into a sample for analysis (in-sample) and a sample for testing (out-of-sample), to avoid over-fitting to the data.
I made the choice to ...
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1answer
50 views
LSTM regression bias increases when targets go close to 0
I've build a LSTM model for time series forecasting. Results are not bad, with a mean normalized error of 7%. However, this normalized bias shows a clear pattern: The closer to 0 the value to predict, ...
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0answers
19 views
Suitable algorithm for forecasting demand from mixed seasonal/non-seasonal data
I am new to data science and have an interesting forecasting challenge.
Suppose I am a publisher of text books and related study materials (mock exam papers, revision notes and so on) for different ...
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2answers
124 views
Multivariate VAR model: ValueError: x already contains a constant
I have already read this question and the associated answer.
I have removed any 'all zero' columns, as recommended in the answer. I have 3,169 columns remaining.
...
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1answer
16 views
Autoregression with multiple factors
I am not sure if this is the right place to ask this question. Anyway, I am working on a Forecasting using spending data. Using autoregression, I am able to predict the following number decently well ...
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0answers
34 views
What kind of algorithm should I use to build ML model that can predict just next reoccurence of an event in the future (at irregular time interval)?
I'm quite new to machine learning and statistics.
I've a dataset from some ecommerce sale's history. It's almost 2k instances, and features include personId (string), productCategory (string/...
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1answer
32 views
How is PACF analysis output related to LSTM ?
I was going through a recent paper “A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature ForecastingUsing Long Short-Term Memory Neural Network Based on Ensemble Empirical ...
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1answer
254 views
LSTM future steps prediction with shifted y_train relatively to X_train
I'm trying to predict simple one feature time series data with shifted train data. The source looks like this:
...
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2answers
32 views
How to predict weather? [closed]
I want to know how does one predict day to day weathers. Like what are the factors that must be known to predict day to day weathers using a NN. Few factors I could think of are:
Humidity
Weather ...
1
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0answers
25 views
Can ARIMA be applied on a dataset of few months?
I have dataset of a few months of (time series) electrical load usage of multiple users. Can/Should ARIMA be applied for load forecasting considering that its not much data?
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2answers
318 views
VAR model ValueError: x already contains a constant
I'm using VAR model for multivariate time series. The structure is that although each variable is a linear function of past lags of itself and past lags of the other variables, one and/or two of the ...
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2answers
148 views
forecasting revenue
Does anyone have any recommendations on how I would go about forecasting Microsofts revenue using python + time series or ML (recommended techniques e.g Random-forest). (I have past revenue and share ...
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1answer
967 views
Can Reinforcement learning be applied for time series forecasting?
Can Reinforcement learning be applied for time series forecasting?
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1answer
144 views
demand forecast for B2B
I am attempting to create a demand forecasting model in python to predict future sales of a particular category of product, using historical sales data.
We are a B2B company, which means that we ...
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1answer
40 views
How to utilize user survey answers and the actual usage in forecasting power usage using LSTM?
I have the pre-trial survey and post-trial survey conducted of around 5000 users for Smart Meter installation.
With this I have power usage reading recorded every 30 min for around one and a half ...
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1answer
82 views
A timestep's prediction depends on future data
Consider an LSTM model with 100 timesteps, each of which with input and target data. Let f(99) be the function mapping the input data of the 99th timestep and hidden state of the 98th timestep to the ...
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35 views
Time series forecasting on log-transformed data
I have a question about time series forecasting on log-transformed data.
The time series looks like this when plotting diff(label):
To make the the variance stationary, I transformed it as follows: ...
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0answers
26 views
Analysing spikes in demand to forecast future demand
I am trying to develop a system to predict future demand for different products at my firm. Currently, I have about two years of weekly sales data of a single product. My target is to predict the ...
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0answers
18 views
How to decide which forecasting model to use? [closed]
So I have a sales forecasting problem where I have 3 years worth of data about weekly sales of a certain company.
There are 3 types of retail stores in that company, Type A, B, and C. There are a ...
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0answers
82 views
Predict the probability that a customer buy today
My company sells a single product that is a commodity. If you buy it, I can be sure that you will buy it again in the near future from me or from my competitors. The demand is affected by the weather. ...
3
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1answer
545 views
Time series forecasting using multiple time series as training data
I am trying to forecast the total attendance (ie. the number of entrances, which is also the number of tickets bought) of a festival just two days after it started. That is, knowing how many people ...
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3answers
154 views
Forecast vs Prediction: What is the difference?
I use the two terms as follows:
A prediction model gets features (which can be a time series) as input and gives a fixed-length output (might be multiple values, but "atomic" in some sense)
Examples:...
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1answer
64 views
How do I use rnn to forecast to n periods with limited data?
So this is my 1st time trying to run a small time-series dataset through an RNN, but after a lot of searching, I haven't been able to find,
1. How I can use this to forecast to n periods ? (like in ...
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0answers
1k views
2
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1answer
62 views
How to predict next year's gross revenue given this year's data?
I have the following dataset (.csv format) which contains:
100 columns: $\textbf{Period}$ (in years, e.g. 2017, 2018, ..., 2028), $\textbf{Gross Revenue}$, $\textbf{Region}$ (e.g. APAC, NEMEA, etc), ...
2
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1answer
203 views
how to decide categorical variables for prediction
I have a dataset that contains weekly sales for stores and categories. It looks like this:
I would like to apply gradient boosting method to predict weekly sales. My question is: Should I create ...
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1answer
24 views
Last cell in recurrent network always the most accurate
I am using a recurrent network for time series forecasting.
The prediction from the last cell in the network always seems to be the most accurate. For example if I have 20 cells (so my input samples ...
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0answers
36 views
What values should I choose for P and Q?
I am trying to forecast energy data generated by LoadProfileGenerater. This data is generated every half hour for 2 years. I am following this tutorial.
I have checked it for stationarity using ...
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1answer
4k views
How to Predict the future values of time horizon with Keras?
I just built this LSTM neural network with Keras
...
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1answer
63 views
What are the prerequisites before running Holt Winters Model?
I just read Demand-Driven Forecasting: A Structured Approach to Forecasting(Wiley and SAS Business Series) and have a few doubts in Holt-Winters Model:
1) Unlike OLS Regression Modeling technique or ...
2
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2answers
139 views
Forecasting Multiple (few hundreds) uni-variate time series with inflated zeros
Hello Practitioners,
Being a newbie seeking help to gain experience in Data Science.
Lets take a scenario where a big company wants to forecast its sales(a specific product) across different ...
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0answers
2k views
time series forecasting - sliding window method
I have been trying to understand this sliding window technique but to no avail and really unsure as to how I would implement it.
My dataset: I have hourly values for the electric load for a year (...
0
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1answer
39 views
Need recommandations of using timing variables for forecasting sales
I have a data.table that contains many timing variables.
Date: it gives the date of Sales
Promo2(week, year): It describes the calendar week and the year when the store has started to participate in ...
0
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0answers
267 views
SVR (sklearn) - getting same values in prediction
https://notebooks.azure.com/ML-beginner/libraries/1SVR
Not sure, what's going here. I'm getting the same values for the variable I want to predict - peak electrical load.
X is date, y is peak load.
...
2
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2answers
89 views
How do I arrange my data to predict 6 weeks of daily sales
I have a data.table base that has many variables to use them to forecasting sales for the next 6 weeks of daily sales. In fact, all the database is arranged by date as you can see here.Note that here ...
3
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1answer
84 views
When forecasting time series, how does one incorporate the test data back into the model after training?
When you build a classification or regression model, you typically split the data into a train data set and a test data set. The test data is a randomly selected subset of the overall data.
Once you ...
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0answers
268 views
Spark fitting into Data Science Paradigm for timeseries data
I have seen Dataframe as new API on Spark2 instead of RDD.So I have following few question about the utility of Spark in terms of time series data.
Is forecasting still limited to memory available in ...
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1answer
244 views
Long term time series forecasts with small dataset
I have a small dataset which has timestamp and temperature values for 6 months(I.e. one temperature value per day). I would like to forecast 2-3 months of temperature.
I would like to know, what kind ...
