Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Now updated with Dask to handle millions of rows.

Overview

auto-ts

Auto_TS: Auto_TimeSeries

Automatically build multiple Time Series models using a Single Line of Code. Now updated with Dask.

Auto_timeseries is a complex model building utility for time series data. Since it automates many Tasks involved in a complex endeavor, it assumes many intelligent defaults. But you can change them. Auto_Timeseries will rapidly build predictive models based on Statsmodels ARIMA, Seasonal ARIMA and Scikit-Learn ML. It will automatically select the best model which gives best score specified.

New version 0.0.35 onwards has major updates: You can now load your file into Dask dataframes. Just provide the name of your file and if it is too large to fit into a pandas dataframe, Auto_TS will automatically detect and load it into a Dask dataframe.

Also, new since version 0.0.25 is the syntax of Auto_TimeSerie: It is now more like scikit-learn (with fit and predict). You will have to initialize an object and then call fit with your data and then predict again with data. Hope this makes it easier to remember and use.

Introduction

Auto_TimeSeries enables you to build and select multiple time series models using techniques such as ARIMA, SARIMAX, VAR, decomposable (trend+seasonality+holidays) models, and ensemble machine learning models.

Auto_TimeSeries is an Automated ML library for time series data. Auto_TimeSeries was initially conceived and developed by Ram Seshadri and was significantly expanded in functionality and scope and upgraded to its present status by Nikhil Gupta.

auto-ts.Auto_TimeSeries is the main function that you will call with your train data. You can then choose what kind of models you want: stats, ml or FB prophet based model. You can also tell it to automatically select the best model based on the scoring parameter you want it to be based on. It will return the best model and a dictionary containing predictions for the number of forecast_periods you mentioned (default=2).

INSTALLATION INSTRUCTIONS

  1. Use “pip install auto-ts”
  2. Use “pip3 install auto-ts” if the above doesn’t work
  3. pip install git+git://github.com/AutoViML/Auto_TS

Note for Windows Users

Windows users may experience difficulties with the fbprophet and pystan dependency installations. Because of this, we recommend installing fbprophet using instructions from the fbprophet documentation page prior to installing auto-ts. For Anaconda users, this can be accomplished via:

  1. conda install -c conda-forge fbprophet

  2. pip install auto-ts

RUN

First you need to import auto_timeseries from auto_ts library:
from auto_ts import auto_timeseries

Second, Initialize an auto_timeseries model object which will hold all your parameters:

model = auto_timeseries( score_type='rmse', time_interval='Month', non_seasonal_pdq=None, seasonality=False, seasonal_period=12, model_type=['Prophet'], verbose=2)

Here are how the input parameters defined:
  1. score_type (default='rmse'): The metric used for scoring the models. Type is string. Currently only the following two types are supported: (1) "rmse": Root Mean Squared Error (RMSE) (2) "normalized_rmse": Ratio of RMSE to the standard deviation of actuals
  2. time_interval (default is None): Used to indicate the frequency at which the data is collected. This is used for two purposes (1) in building the Prophet model and (2) used to impute the seasonal period for SARIMAX in case it is not provided by the user (None). Type is String. We use the following pandas date range frequency aliases that Prophet uses to make the prediction dataframe.

    Hence, please note that these are the list of allowed aliases for frequency: ['B','C','D','W','M','SM','BM','CBM', 'MS','SMS','BMS','CBMS','Q','BQ','QS','BQS', 'A,Y','BA,BY','AS,YS','BAS,BYS','BH', 'H','T,min','S','L,ms','U,us','N']

    For a start, you can test the following codes for your data and see how the results are:

    • 'MS', 'M', 'SM', 'BM', 'CBM', 'SMS', 'BMS' for monthly frequency data
    • 'D', 'B', 'C' for daily frequency data
    • 'W' for weekly frequency data
    • 'Q', 'BQ', 'QS', 'BQS' for quarterly frequency data
    • 'A,Y', 'BA,BY', 'AS,YS', 'BAS,YAS' for yearly frequency data
    • 'BH', 'H', 'h' for hourly frequency data
    • 'T,min' for minute frequency data
    • 'S', 'L,milliseconds', 'U,microseconds', 'N,nanoseconds' for second frequency data
    Or you can leave it as None and auto_timeseries will try and impute it.
  3. non_seasonal_pdq (default = (3,1,3)): Indicates the maximum value of (p, d, q) to be used in the search for statistical ARIMA models. If None, then the following values are assumed max_p = 3, max_d = 1, max_q = 3. Type is Tuple.
  4. seasonality (default=False): Used in the building of the SARIMAX model only at this time. True or False. Type is bool.
  5. seasonal_period (default is None): Indicates the seasonal period in your data. This depends on the peak (or valley) period that occurs regularly in your data. Used in the building of the SARIMAX model only at this time. There is no impact of this argument if seasonality is set to False If None, the program will try to infer this from the time_interval (frequency) of the data We assume the following as defaults but feel free to change them. (1) If frequency is Monthly, then seasonal_period is assumed to be 12 (1) If frequency is Daily, then seasonal_period is assumed to be 30 (but it could be 7) (1) If frequency is Weekly, then seasonal_period is assumed to be 52 (1) If frequency is Quarterly, then seasonal_period is assumed to be 4 (1) If frequency is Yearly, then seasonal_period is assumed to be 1 (1) If frequency is Hourly, then seasonal_period is assumed to be 24 (1) If frequency is Minutes, then seasonal_period is assumed to be 60 (1) If frequency is Seconds, then seasonal_period is assumed to be 60 Type is integer
  6. conf_int (default=0.95): Confidence Interval for building the Prophet model. Default: 0.95. Type is float.
  7. model_type (default: 'stats': The type(s) of model to build. Default to building only statistical models Can be a string or a list of models. Allowed values are: 'best', 'prophet', 'stats', 'ARIMA', 'SARIMAX', 'VAR', 'ML'. "prophet" will build a model using FB Prophet -> this means you must have FB Prophet installed "stats" will build statsmodels based ARIMA, SARIMAX and VAR models "ML" will build a machine learning model using Random Forests provided explanatory vars are given 'best' will try to build all models and pick the best one If a list is provided, then only those models will be built WARNING: "best" might take some time for large data sets. We recommend that you choose a small sample from your data set before attempting to run entire data. Type can be either: [string, list]
  8. verbose (default=0): Indicates the verbosity of printing (Default = 0). Type is integer.
The next step after defining the model object is to fit it with some real data:


model.fit( traindata=train_data, ts_column=ts_column, target=target, cv=5, sep="," )


Here are how the parameters defined:
  • traindata (required): It can be either a dataframe or a file. You must give the name of the file along with its data path in case if a file. It also accepts a pandas dataframe in case you already have a dataframe loaded in your notebook.
  • ts_column (required): name of the datetime column in your dataset (it could be a name of column or index number in the columns index)
  • target (required): name of the column you are trying to predict. Target could also be the only column in your dataset
  • cv (default=5): You can enter any integer for the number of folds you want in your cross validation data set.
  • sep (default=","): Sep is the separator in your traindata file. If your separator is ",", "\t", ";", make sure you enter it here. If not, it is ignored.
The next step after training the model object is to make some predictions with test data:


predictions = model.predict( testdata = can be either a dataframe or an integer standing for the forecast_period, model = 'best' or any other string that stands for the trained model )

Here are how the parameters are defined. You can choose to send either testdata in the form of a dataframe or send in an integer to decide how many periods you want to forecast. You need only
  • testdata (required): It can be either a dataframe containing test data or you can use an integer standing for the forecast_period (you want).
  • model (optional, default = 'best'): The name of the model you want to use among the many different models you have trained. Remember that the default is the best model. But you can choose any model that you want to forecast with. Type is String.

Requirements

dask

scikit-learn

FB Prophet

statsmodels

pmdarima

XGBoost

License:

Apache License 2.0

Recommendations

  • We recommend that you choose a small sample from your data set before attempting to run entire data. and the evaluation metric so it can select the best model. Currently models within “stats” are compared using AIC and BIC. However, models across different types are compared using RMSE. The results of models are shown using RMSE and Normalized RMSE (ratio of RMSE to the standard deviation of actuals).
  • You must clean the data and not have any missing values. Make sure the target variable is numeric, otherwise, it won’t run. If there is more than one target variable in your data set, just specify only one for now, and if you know the time interval that is in your data, you can specify it. Otherwise it auto-ts will try to infer the time interval on its own.
  • If you give Auto_Timeseries a different time interval than what the data has, it will automatically resample the data to the given time interval and use the mean of the target for the resampled period.
  • Notice that except for filename and ts_column input arguments, which are required, all other arguments are optional.
  • Note that optionally you can give a separator for the data in your file. Default is comma (",").
  • “time_interval” options are any codes that you can find in this page below. Pandas date-range frequency aliases
  • Optionally, you can give seasonal_period as any integer that measures the seasonality in the data. If not given, seasonal_period is assumed automatically as follows: Months = 12, Days = 30, Weeks = 52, Qtr = 4, Year = 1, Hours = 24, Minutes = 60 and Seconds = 60.
  • If you want to give your own non-seasonal order, please input it as non_seasonal_pdq and for seasonal order, use seasonal_PDQ as the input. Use tuples. For example, seasonal_PDQ = (2,1,2) and non_seasonal_pdq = (0,0,3). It will accept only tuples. The default is None and Auto_Timeseries will automatically search for the best p,d,q (for Non Seasonal) and P, D, Q (for Seasonal) orders by searching for all parameters from 0 to 12 for each value of p,d,q and 0-3 for each P, Q and 0-1 for D.

DISCLAIMER:

This is not an Officially supported Google project.

© Google

Comments
  • AttributeError: 'numpy.ndarray' object has no attribute 'columns'

    AttributeError: 'numpy.ndarray' object has no attribute 'columns'

    model.fit not working I was using this library for thepast 4-5 days , every thing was working fine but now suddently getting an error in fitting the model model.fit(traindata=train, ts_column='Date', target='production', cv=3)

    ERROR :

    Start of Fit..... Target variable given as = production Start of loading of data..... Inputs: ts_column = Date, sep = ,, target = ['production'] Using given input: pandas dataframe... Date column exists in given train data... train data shape = (173, 14) Alert: Could not detect strf_time_format of Date. Provide strf_time format during "setup" for better results.

    Running Augmented Dickey-Fuller test with paramters: maxlag: 31 regression: c autolag: BIC

    AttributeError Traceback (most recent call last) in () ----> 1 model.fit(traindata=train, ts_column='Date', target='production', cv=3)

    2 frames /root/.local/lib/python3.7/site-packages/auto_ts/utils/eda.py in check_each_var_for_stationarity(time_df, autolag, verbose) 319 alpha = 0.05 320 all_vars = 1 --> 321 copy_cols = time_df.columns.tolist() 322 for each_var in copy_cols: 323 timeseries = time_df[each_var].values

    AttributeError: 'numpy.ndarray' object has no attribute 'columns'

    opened by pankajmdan 15
  • Variation in SARIMAX and Prophet forecast dates

    Variation in SARIMAX and Prophet forecast dates

    When SARIMAX is the best output and the forecast period =12... it forecasts [defaultdict output] for 12 periods in the train data instead of 12 forward periods while Prophet includes 12 forward periods.

    Best Model is: SARIMAX Best Model Forecasts: net_revenue mean mean_se mean_ci_lower mean_ci_upper 2018-07-31 3.080343 0.366081 2.362838 3.797848 2018-08-31 2.808159 0.366107 2.090602 3.525715 2018-09-30 2.893071 0.367888 2.172024 3.614118 2018-10-31 3.178024 0.375812 2.441446 3.914603 2018-11-30 3.154994 0.379846 2.410510 3.899477 2018-12-31 3.122128 0.381915 2.373588 3.870667 2019-01-31 2.917377 0.382981 2.166749 3.668006 2019-02-28 3.091802 0.383531 2.340096 3.843509 2019-03-31 3.260538 0.383815 2.508274 4.012801 2019-04-30 3.008322 0.383962 2.255770 3.760873 2019-05-31 3.490022 0.384038 2.737322 4.242723 2019-06-30 3.180734 0.384077 2.427956 3.933511 Best Model Score: 0.39

    bug enhancement 
    opened by raghurajpandya 15
  • feature selection in time series model

    feature selection in time series model

    Hello contributors,

    When I used the time series model such as var model or sarimax model in multivariate time series , after fitting the model and printing the model.summary(), the information seemed that model automatically did the feature selection. However, when I read the source codes such as build_var, and init , I could not figure out what kinds of method that the package uses to select features. For example, there are 6 time series as inputs, but the model summary only displayed the information between one input and the dependent variable, in other words, the output.

    Does the package implements any feature selection function when using time series model such as var or sarimax? If the answer is "Yes", where are the detailed parts? Thank you.

    opened by kennis222 7
  • local variable 'forecast_df_folds' referenced before assignment

    local variable 'forecast_df_folds' referenced before assignment

    Running into issue when running:

    model.fit( traindata=dataset, # traindata=file_path, # Alternately, you can specify the file directly ts_column=ts_column, target=target, cv=3, sep=sep)

    I get the error: local variable 'forecast_df_folds' referenced before assignment

    Exception occurred while building Prophet model... Prophet object can only be fit once. Instantiate a new object. FB Prophet may not be installed or Model is not running... Traceback (most recent call last):

    File "", line 33, in model.fit(

    File "C:\Users\myname\anaconda3\lib\site-packages\auto_ts_init_.py", line 506, in fit self.ml_dict[name]['forecast'] = forecast_df_folds

    UnboundLocalError: local variable 'forecast_df_folds' referenced before assignment

    opened by Vaderv 7
  • Testdata error

    Testdata error

    Hi gents, When i run model. Predict a silly error comes up. It is related to the parameter testdata and returns object of type float has no len() Please let me know if you have a quick solution. Thanks for your help.

    opened by Hydaspex 6
  • model fit method throws error

    model fit method throws error "local variable 'forecast_df_folds' referenced before assignment"

    I installed the package using pip and then cloned the repo to test run the example notebook. It throws the following error. Start of Fit..... Running Augmented Dickey-Fuller test with paramters: maxlag: 5 regression: c autolag: BIC Results of Augmented Dickey-Fuller Test: +-----------------------------+------------------------------+ | | Dickey-Fuller Augmented Test | +-----------------------------+------------------------------+ | Test Statistic | -3.2721577323655944 | | p-value | 0.01616983190458116 | | #Lags Used | 1.0 | | Number of Observations Used | 43.0 | | Critical Value (1%) | -3.5925042342183704 | | Critical Value (5%) | -2.931549768951162 | | Critical Value (10%) | -2.60406594375338 | +-----------------------------+------------------------------+ this series is stationary Target variable given as = Sales Start of loading of data..... Input is data frame. Performing Time Series Analysis ts_column: Time Period sep: , target: Sales Loaded pandas dataframe... pandas Dataframe loaded successfully. Shape of data set = (45, 2) chart frequency not known. Continuing... Time Interval between observations has not been provided. Auto_TS will try to infer this now... Time series input in days = 31 It is a Monthly time series. WARNING: Running best models will take time... Be Patient...

    ================================================== Building Prophet Model

    Running Facebook Prophet Model... Fit-Predict data (shape=(45, 3)) with Confidence Interval = 0.95... Starting Prophet Fit No seasonality assumed since seasonality flag is set to False Starting Prophet Cross Validation Max. iterations using expanding window cross validation = 3

    Fold Number: 1 --> Train Shape: 30 Test Shape: 5 Root Mean Squared Error predictions vs actuals = 42.30 Std Deviation of actuals = 126.63 Normalized RMSE = 33% Cross Validation window: 1 completed

    Fold Number: 2 --> Train Shape: 35 Test Shape: 5 Root Mean Squared Error predictions vs actuals = 20.71 Std Deviation of actuals = 68.89 Normalized RMSE = 30% Cross Validation window: 2 completed

    Fold Number: 3 --> Train Shape: 40 Test Shape: 5 Root Mean Squared Error predictions vs actuals = 53.09 Std Deviation of actuals = 82.02 Normalized RMSE = 65% Cross Validation window: 3 completed


    Model Cross Validation Results:

    MAE (as % Std Dev of Actuals) = 25.56%
    MAPE (Mean Absolute Percent Error) = 5%
    RMSE (Root Mean Squared Error) = 40.9755
    Normalized RMSE (MinMax) = 11%
    Normalized RMSE (as Std Dev of Actuals)= 31%
    

    Time Taken = 6 seconds Exception occurred while building Prophet model... Prophet object can only be fit once. Instantiate a new object. FB Prophet may not be installed or Model is not running...

    UnboundLocalError Traceback (most recent call last) in ----> 1 model.fit( 2 traindata=train, 3 ts_column=ts_column, 4 target=target, 5 cv=3,

    ~\Anaconda3\lib\site-packages\auto_ts_init_.py in fit(self, traindata, ts_column, target, sep, cv) 504 505 self.ml_dict[name]['model'] = model --> 506 self.ml_dict[name]['forecast'] = forecast_df_folds 507 self.ml_dict[name][self.score_type] = score_val 508 self.ml_dict[name]['model_build'] = model_build

    UnboundLocalError: local variable 'forecast_df_folds' referenced before assignment

    opened by ashish-cerv 6
  • Query about ML model: testdata should be entered as input

    Query about ML model: testdata should be entered as input

    Hi contributor:

    I have read the multivariate_example.ipynb, but I still have confusion. Unlike ar_based model, we need to input the test dataframe when we are doing prediction.

    For example, after the UCI dataset, the date is from 2004-03-10 to 2005-04-04. I would like to predict the output from 2005-03-06 to 2005-04-04 which lasts 30 days. Is correct as mention below:

    training_data = length of data from 2004-03-10 - 2005-02-03 test_data = length of data from 2005-02-04 to 2005-03-05 which lasts 30 days predict_results = tree_model.predict(test_data, model='ML') Are predict_results from 2005-03-06 to 2005-04-04? actual_data to compare with the predict_results: 2005-03-06 to 2005-04-04

    BUT what I get is from 2005-02-04 to 2005-03-05: Screen Shot 2022-01-27 at 11 35 51 AM Screen Shot 2022-01-27 at 11 36 03 AM

    opened by kennis222 5
  • Type of time series column datetime is float or unknown. Must be string or datetime. Please check input and try again.

    Type of time series column datetime is float or unknown. Must be string or datetime. Please check input and try again.

    <class 'pandas.core.frame.DataFrame'> Int64Index: 101952 entries, 0 to 103391 Data columns (total 8 columns):

    Column Non-Null Count Dtype


    0 datetime 101952 non-null datetime64[ns] 1 load 101952 non-null float64
    2 apparent_temperature 101952 non-null float64
    3 temperature 101952 non-null float64
    4 humidity 101952 non-null float64
    5 dew_point 101952 non-null float64
    6 wind_speed 101952 non-null float64
    7 cloud_cover 101952 non-null float64
    dtypes: datetime64ns, float64(7) memory usage: 7.0 MB

    sample data

    datetime load apparent_temperature temperature humidity dew_point wind_speed cloud_cover 0 2018-01-01 00:00:00 803.22270 10.45800 10.45800 0.955500 8.946000 0.0 0.0 1 2018-01-01 00:15:00 774.89523 10.32675 10.32675 0.961625 8.911875 0.0 0.0 2 2018-01-01 00:30:00 731.46927 10.19550 10.19550 0.967750 8.877750 0.0 0.0 3 2018-01-01 00:45:00 713.93870 10.06425 10.06425 0.973875 8.843625 0.0 0.0 4 2018-01-01 01:00:00 699.23007 9.93300 9.93300 0.980000 8.809500 0.0 0.0

    model = auto_timeseries( score_type='rmse', time_interval='T,min', non_seasonal_pdq=None, seasonality=True, seasonal_period=60, model_type=['Prophet'], verbose=2)

    model.fit( traindata=train, ts_column="datetime", target="load", cv=5, sep="," )

    While training I emncounter the below error : Start of Fit..... Running Augmented Dickey-Fuller test with paramters: maxlag: 31 regression: c autolag: BIC Results of Augmented Dickey-Fuller Test: +-----------------------------+------------------------------+ | | Dickey-Fuller Augmented Test | +-----------------------------+------------------------------+ | Test Statistic | -15.883436634196332 | | p-value | 8.704767665249516e-29 | | #Lags Used | 27.0 | | Number of Observations Used | 101924.0 | | Critical Value (1%) | -3.430414160204652 | | Critical Value (5%) | -2.8615683578111595 | | Critical Value (10%) | -2.5667850938695476 | +-----------------------------+------------------------------+ this series is stationary Target variable given as = load Start of loading of data..... Input is data frame. Performing Time Series Analysis ts_column: datetime sep: , target: load Using given input: pandas dataframe... datetime column exists in given train data... Type of time series column datetime is float or unknown. Must be string or datetime. Please check input and try again.

    TypeError Traceback (most recent call last) in () ----> 1 model.fit( traindata=train, ts_column="datetime", target="load", cv=5, sep="," )

    1 frames /usr/local/lib/python3.7/dist-packages/auto_ts/init.py in fit(self, traindata, ts_column, target, sep, cv) 309 dask_df, ts_df = load_ts_data(traindata, self.ts_column, sep, target, self.dask_xgboost_flag) 310 else: --> 311 _, ts_df = load_ts_data(traindata, self.ts_column, sep, target, self.dask_xgboost_flag) 312 if isinstance(ts_df, str): 313 print("""Time Series column '%s' could not be converted to a Pandas date time column.

    /usr/local/lib/python3.7/dist-packages/auto_ts/utils/etl.py in load_ts_data(filename, ts_column, sep, target, dask_xgboost_flag) 51 dft, _ = change_to_datetime_index(dft, ts_column) 52 ### you have to change the pandas df also to datetime index ### ---> 53 filename, _ = change_to_datetime_index(filename, ts_column) 54 #preds = [x for x in list(dft) if x not in [target]] 55 #dft = dft[[target]+preds]

    TypeError: cannot unpack non-iterable NoneType object

    Note the datetime column is in datetimeformat , even then it throws error. Could you please help me with the resolution AssignmentData.csv

    opened by plaban1981 5
  • Error while training auto_ts:  Line search cannot locate an adequate point after 20 function and gradient evaluations

    Error while training auto_ts: Line search cannot locate an adequate point after 20 function and gradient evaluations

    I'm getting this error, while running auto_ts model training on Sales_and_Marketing.csv

    Tit   = total number of iterations
    Tnf   = total number of function evaluations
    Tnint = total number of segments explored during Cauchy searches
    Skip  = number of BFGS updates skipped
    Nact  = number of active bounds at final generalized Cauchy point
    Projg = norm of the final projected gradient
    F     = final function value
    
               * * *
    
       N    Tit     Tnf  Tnint  Skip  Nact     Projg        F
        9     29     32      1     0     0   2.301D-04   1.150D+01
      F =   11.498879635408754
    
    CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
    
     Line search cannot locate an adequate point after 20 function
      and gradient evaluations.  Previous x, f and g restored.
     Possible causes: 1 error in function or gradient evaluation;
                      2 rounding error dominate computation.
     This problem is unconstrained.
     This problem is unconstrained.
    
     Bad direction in the line search;
       refresh the lbfgs memory and restart the iteration.
     This problem is unconstrained.
    
     Bad direction in the line search;
       refresh the lbfgs memory and restart the iteration.
    
     Bad direction in the line search;
       refresh the lbfgs memory and restart the iteration.
     This problem is unconstrained.
    
     Line search cannot locate an adequate point after 20 function
      and gradient evaluations.  Previous x, f and g restored.
     Possible causes: 1 error in function or gradient evaluation;
                      2 rounding error dominate computation.
     This problem is unconstrained.
    
     Line search cannot locate an adequate point after 20 function
      and gradient evaluations.  Previous x, f and g restored.
     Possible causes: 1 error in function or gradient evaluation;
                      2 rounding error dominate computation.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
    
     Line search cannot locate an adequate point after 20 function
      and gradient evaluations.  Previous x, f and g restored.
     Possible causes: 1 error in function or gradient evaluation;
                      2 rounding error dominate computation.
     This problem is unconstrained.
    
     Bad direction in the line search;
       refresh the lbfgs memory and restart the iteration.
    
     Bad direction in the line search;
       refresh the lbfgs memory and restart the iteration.
    
     Line search cannot locate an adequate point after 20 function
      and gradient evaluations.  Previous x, f and g restored.
     Possible causes: 1 error in function or gradient evaluation;
                      2 rounding error dominate computation.
     This problem is unconstrained.
    
     Bad direction in the line search;
       refresh the lbfgs memory and restart the iteration.
    
     Warning:  more than 10 function and gradient
       evaluations in the last line search.  Termination
       may possibly be caused by a bad search direction.
     This problem is unconstrained.
    
     Line search cannot locate an adequate point after 20 function
      and gradient evaluations.  Previous x, f and g restored.
     Possible causes: 1 error in function or gradient evaluation;
                      2 rounding error dominate computation.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
    
     Line search cannot locate an adequate point after 20 function
      and gradient evaluations.  Previous x, f and g restored.
     Possible causes: 1 error in function or gradient evaluation;
                      2 rounding error dominate computation.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
    
     Line search cannot locate an adequate point after 20 function
      and gradient evaluations.  Previous x, f and g restored.
     Possible causes: 1 error in function or gradient evaluation;
                      2 rounding error dominate computation.
     This problem is unconstrained.
    
     Line search cannot locate an adequate point after 20 function
      and gradient evaluations.  Previous x, f and g restored.
     Possible causes: 1 error in function or gradient evaluation;
                      2 rounding error dominate computation.
     This problem is unconstrained.
     This problem is unconstrained.
    
     Line search cannot locate an adequate point after 20 function
      and gradient evaluations.  Previous x, f and g restored.
     Possible causes: 1 error in function or gradient evaluation;
                      2 rounding error dominate computation.
     This problem is unconstrained.
    
     Line search cannot locate an adequate point after 20 function
      and gradient evaluations.  Previous x, f and g restored.
     Possible causes: 1 error in function or gradient evaluation;
                      2 rounding error dominate computation.
     This problem is unconstrained.
    
     Line search cannot locate an adequate point after 20 function
      and gradient evaluations.  Previous x, f and g restored.
     Possible causes: 1 error in function or gradient evaluation;
                      2 rounding error dominate computation.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
    
     Warning:  more than 10 function and gradient
       evaluations in the last line search.  Termination
       may possibly be caused by a bad search direction.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
    
     Warning:  more than 10 function and gradient
       evaluations in the last line search.  Termination
       may possibly be caused by a bad search direction.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
    
     Warning:  more than 10 function and gradient
       evaluations in the last line search.  Termination
       may possibly be caused by a bad search direction.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
     This problem is unconstrained.
    

    any idea, how to solve this issue?

    opened by vreddy-cs 5
  • ML model

    ML model

    Nice work! Just wondering, if in future, there will be additional ML models added like LSTM or CNN etc., other than what is currently present, which is Random Forest?

    I actually encountered this error while "pip installing"

    ERROR: Command errored out with exit status 1:

    opened by zneha 5
  • local variable 'mean_cv_score' referenced before assignment

    local variable 'mean_cv_score' referenced before assignment


    UnboundLocalError Traceback (most recent call last) in ----> 1 model.fit( traindata=df_after, ts_column='Timestamp',target='Weighted_Price')

    /usr/local/lib/python3.8/dist-packages/auto_ts/init.py in fit(self, traindata, ts_column, target, sep, cv) 750 print(f"Total time taken: {elapsed:.0f} seconds.") 751 print("-"*50 + "\n\n") --> 752 print("Leaderboard with best model on top of list:\n",self.get_leaderboard()) 753 return self 754

    /usr/local/lib/python3.8/dist-packages/auto_ts/init.py in get_leaderboard(self, ascending) 904 # else: # Assuming List 905 # mean_cv_score = sum(cv_scores)/len(cv_scores) --> 906 mean_cv_scores.append(mean_cv_score) 907 908 results = pd.DataFrame({"name": names, self.score_type: mean_cv_scores})

    UnboundLocalError: local variable 'mean_cv_score' referenced before assignment


    So I think the problem is around line 898 in init.py 897 elif isinstance(cv_scores, list): 898 if len(cv_scores) == 0: 899 mean_cv_score = np.inf 900 else: 901 mean_cv_score = self.__get_mean_cv_score(cv_scores) 902 # if isinstance(cv_scores, float): 903 # mean_cv_score = cv_scores 904 # else: # Assuming List 905 # mean_cv_score = sum(cv_scores)/len(cv_scores) because it doesn't assign the value of mean_cv_score if len(cv_scores) is not equal to 0,I'm not an expert to this frame, but I saw a comment in line 905 says 'assuming list',maybe should put it below 899 like 897 elif isinstance(cv_scores, list): 898 if len(cv_scores) == 0: 899 mean_cv_score = np.inf 900 else: 901 mean_cv_score = sum(cv_scores)/len(cv_scores)

    Just my guess

    opened by tjc0726 4
  • Update build_prophet.py

    Update build_prophet.py

    fbprophet doesn't work yet.

    https://github.com/dask/dask-examples/pull/192

    I changed fbprophet to prophet. Because my notebook doesnt works with there.

    opened by turkalpmd 0
  • Updating to New Version of Prophet

    Updating to New Version of Prophet

    Prophet is now on version 1.0.1, in which it is called prophet instead of fbprophet. This updates that and allows it to run well with the updated package. Some verbiage may need to be updated within the comments of the main files to reflect this as well.

    Additionally, I was unable to find how to update the install so that the newer version of prophet is installed when one pip installs prophet, but I could not figure out how to make this edit. if someone could help with that, I would appreciate it.

    opened by KarateKat94 2
  • Prophet 1.0.1 vs fbProphet

    Prophet 1.0.1 vs fbProphet

    Would we be able to upgrade to the newer version of Prophet, that is now just called prophet? It seems like it would be an easy fix overall that I would be happy to take on.

    opened by ghost 8
  • Modular Unit Tests

    Modular Unit Tests

    Break down the auto_ts unit tests into more modular unit tests. IT currently runs like regression tests and not unit tests

    TODO

    • [x] arima
    • [ ] auto_sarimax
    • [x] var
    • [ ] Prophet
    • [ ] ml
    • [ ] Clean auto_ts tests to check for flow related issues only
    enhancement 
    opened by ngupta23 0
  • BuildProphet - Consistency of output

    BuildProphet - Consistency of output

    Prophet Model

    • [ ] predict function with simple=False returns 16 columns. See if this needs to be made consistent with the statistical models from statsmodels which return only 4 columns (mean, std err, lower ci, upper ci)
    • [ ] Index is called ds in prophet and <ts_column> name in statsmodels. Need to make consistent.
    --------------------------------------------------
    Predictions with Best Model (Prophet)
    --------------------------------------------------
    Building Forecast dataframe. Forecast Period = 8
    Building Forecast dataframe. Forecast Period = 8
    Building Forecast dataframe. Forecast Period = 8
    Building Forecast dataframe. Forecast Period = 8
    Building Forecast dataframe. Forecast Period = 8
               ds       trend  ...  multiplicative_terms_upper        yhat
    40 2014-04-30  651.843432  ...                         0.0  749.061242
    41 2014-05-31  657.531354  ...                         0.0  751.077262
    42 2014-06-30  663.035794  ...                         0.0  796.892366
    43 2014-07-31  668.723715  ...                         0.0  783.206733
    44 2014-08-31  674.411637  ...                         0.0  689.698130
    45 2014-09-30  679.916077  ...                         0.0  595.713426
    46 2014-10-31  685.603998  ...                         0.0  569.486600
    47 2014-11-30  691.108439  ...                         0.0  635.884371
    
    --------------------------------------------------
    Predictions with SARIMAX Model
    --------------------------------------------------
    Sales             mean     mean_se  mean_ci_lower  mean_ci_upper
    2013-09-01  803.316737   57.933329     689.769498     916.863976
    2013-10-01  762.460940   79.971766     605.719158     919.202722
    2013-11-01  718.358193   96.253327     529.705138     907.011248
    2013-12-01  711.421305   96.180308     522.911365     899.931245
    2014-01-01  719.362546   98.272104     526.752761     911.972331
    2014-02-01  732.709819  100.930940     534.888812     930.530825
    2014-03-01  747.576454  102.978924     545.741472     949.411437
    2014-04-01  762.473494  104.292923     558.063121     966.883867
    --------------------------------------------------
    Predictions with VAR Model
    --------------------------------------------------
    Sales             mean     mean_se  mean_ci_lower  mean_ci_upper
    2013-09-01  741.377909   61.808346     620.235777     862.520040
    2013-10-01  676.233419   90.153283     499.536231     852.930608
    2013-11-01  615.538721  105.173723     409.402012     821.675430
    2013-12-01  571.797729  111.305204     353.643538     789.951919
    2014-01-01  546.952783  113.044924     325.388803     768.516763
    2014-02-01  537.342231  113.342418     315.195173     759.489289
    2014-03-01  537.474487  113.443588     315.129140     759.819834
    2014-04-01  542.307393  113.595165     319.664960     764.949825
    ```
    
    
    enhancement 
    opened by ngupta23 1
Releases(0.0.23)
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AutoViz and Auto_ViML
Automated Machine Learning: Build Variant Interpretable Machine Learning models. Project Created by Ram Seshadri.
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